On keeping strong muscles: from the genetic causes to potential treatments for inherited neuromuscular disorders.
Muscles in our bodies have the ability to contract in order to produce force and motion. Three different types of muscles compose our muscular system: cardiac muscles, which are unique to the heart, smooth muscles, which form the supporting tissue around blood vessels and internal organs, and skeletal muscles, most of which are attached to bones and are responsible for movements. Of the three types, only the skeletal muscles are under our voluntary control.
Inherited neuromuscular disorders (NMDs) are a broad range of medical conditions that impair the functions of skeletal muscles. They lead to progressive weakness and loss of muscle mass and eventually to chronic disabilities, representing a burden to patients, their families, and a strain to public healthcare systems. Two large groups of disorders can result in skeletal muscle defects: muscular dystrophies, associated with defects of the muscles themselves, and inherited neuropathies, associated with defects in the nerves that control muscles movements. Like for other genetic disorders, a crucial step in the study of inherited NMDs lies on the identification of genetic mutations responsible for the disease. This is not only important for diagnosis, but also for understanding the physiological mechanisms that become defective in patients, and eventually designing therapies for their treatment. By combining multi-omics, clinical data, biological samples and predictive models to computational biology, scientists at the NeMus department have made important contributions including the development of promising new therapeutic approaches.
Next generation sequencing (NGS) technologies have revolutionized the fields of research and diagnostics over the past decade. The possibility to routinely sequence genomes of more and more patients has provided an invaluable opportunity to collect and cross-reference data and ultimately to identify genes responsible for a given genetic disorder. These technologies have been instrumental to scientists within the NeMus department for the discovery of several genes and gene mutations involved in neuromuscular diseases.
However, analysis of the growing amounts of data generated by NGS and their relationship to complex clinical profiles requires a substantial effort in developing appropriate bioinformatics systems and tools. The "bioinformatics and Genetics" team led by Pr Christophe Béroud has developed various reference systems to address all steps of translational research. They include the most efficient pathogenicity prediction systems (UMD-Predictor and HSF coupled to the VarAFT annotation and filtration system to rapidly identify disease-causing mutations) among the many harmless genetic variations normally found in the genomes of different individuals . Thanks to this expertise, they are leading the clinical bioinformatics aspects of the RD-Connect FP7 European Project and are setting-up the French databases for Copy Number Variations [BAnque Nationale des CNV COnstitutionnels - BANCCO] identified in rare diseases and the French Rare Disease Variant Database (RDVD) collecting all variations identified in NGS experiments [http://rdvd.fr]. In addition, they have established mutation databases for genes involved in cancers, such as the BRCA1/2 genes responsible for breast/ovarian cancers and genetic diseases including NMDs, as well as patient registries, which compile genetic and clinical information for a given disease. For example, the team has developed the TREAT-NMD global registries and a national observatory for patients suffering from Facial Scapulo Humeral Dystrophy (FSHD), a neuromuscular disorder that affects primarily the muscles of the face, shoulders and upper arms. This registry contains today data from over 450 patients, making it the largest FSHD database worldwide.
Understanding the clinical data and the genetics of FSHD has been fundamental in the study of the molecular mechanisms that are defective in this disease, a field that has interested the laboratory of Dr Frédérique Magdinier for several years.
The vast majority of patients with FSHD (95%) carry a genetic mutation on chromosome 4 (at position 4q35). It is located very close to the chromosome end, or telomere, in what is referred to as the subtelomeric region. This region contains a D4Z4-repeat, a stretch of DNA in which a basic unit called D4Z4 is repeated multiple times in tandem. Healthy individuals typically display 11 to 150 D4Z4 units, while FSHD patients display a contraction of the repeat and carry only 1 to 10 D4Z4 units. But this genetic modification is different from those affecting classical genetic diseases, where a mutation is normally found within a gene and alters the gene product in a manner that compromises its function. Instead, contraction of the D4Z4-repeat locally interferes with chromatin, the structure responsible for DNA packaging and compaction inside the nucleus of a cell. Different levels of chromatin compaction are possible, which impact on local accessibility to the information encoded by the DNA: loosely compacted chromatin is permissive of gene expression, while tightly compacted chromatin is not. In healthy individuals, the D4Z4-repeat is associated with tight chromatin and inactive gene expression, but interestingly, both of these characteristics are lost in FSHD patients.
A few years ago, scientists in the laboratory of Dr Frédérique Magdinier revealed that this is due to the ability of the short D4Z4-repeats to interact with CTCF, a protein known to regulate chromatin structure. In addition, the group has shown that shortening of the repeat causes its relocalisation to the nuclear periphery, a nuclear subdomain involved in the regulation of chromatin.
From a clinical point of view, the first signs of FSHD usually appear during the second decade of life. To understand how healthy muscles all of a sudden become affected in FSHD patients, Frédérique Magdinier and her colleagues explored the molecular features of presymptomatic muscles during development.
"We found that foetal muscle displayed defects reminiscent of FSHD adult patient cells ", explains Dr Frédérique Magdinier. "In addition, we also observed a global dysregulation of genes involved in muscle development. These results revealed that even though the symptoms of FSHD only appear as the patients reach adulthood, defects are actually already present during muscle development in foetal life".
In addition, the team has explored large cohorts of patients with typical signs of FSHD as well as asymptomatic carriers and showed that DNA methylation, one of the main epigenetic change associated with the disease is highly variable among individuals. The team has also identified complex chromosomal rearrangements in a subset of patients highlighting the complexity of the disease locus.
These findings have established the importance of studying epigenetic changes and chromatin conformation during muscle development to understand the mechanisms leading to FSHD. Future research is aimed towards a better understanding on both the differentiation of muscle cells and the molecular mechanisms that link the organization of the 4q35 locus to the disease, through the development of cellular models such as induced Pluripotent Stem cells (iPS).
Dysferlinopathies are a group of heterogenous neuromuscular disorders that display highly variable symptoms, ranging from mild to severe functional disabilities. They are all linked to mutations in dysferlin (DYSF), a gene that encodes a large protein important upon damage for repairing the membrane that covers the muscle fibres. To date, over 400 different mutations within the DYSF gene are known to cause disease. Scientists from the MMG have a long experience in diagnosing dysferlinopathies, and have largely contributed to identifying these mutations. The laboratory of Dr Marc Bartoli has recently built a public database that compiles all the mutations found in patients and their relatives, together with the clinical information available for each individual. This database represents a valuable tool for clinicians and scientists for the understanding of this group of disorders.
Beyond their interest in diagnostics, Marc Bartoli and his colleagues have set out to explore therapeutic strategies for the treatment of dysferlinopathies. They have channelled their efforts towards developing a method based on exon-skipping, which takes advantage of the very structure of genes and the way in which they are processed for expression.
To skip or not to skip an exon – how does it work?
Structurally, eukaryotic genes are composed of exons and introns. Exons are the elements that are ultimately expressed and translated into a protein, while introns are non-coding sequences located between exons that are removed before producing the protein. Gene expression starts by transcription, a process in which the information on the DNA is copied into a molecule of another nucleic acid called RNA. Through a mechanism called splicing, the introns are removed from the RNA and the exons are joined together end-to-end. The resulting mature RNA molecule is then translated into the final protein.
Exon-skipping for the treatment of genetic disorders is based on RNA splicing. The idea is to trick the cells into splicing out the exon that carries a genetic mutation such that it is no longer translated. The resulting protein is shorter and may not be fully functional, but in some cases it retains a certain degree of functionality that can at least partially rescue the defects caused by the mutated protein. This method has proven promising for the treatment of another severe genetic disorder characterized by progressive muscle degeneration: Duchenne Muscular Dystrophy (DMD).
So, can exon-skipping be used for the treatment of dysferlinopathies?
Deletion of exon 32 had been found in an individual to cause a very mild version of the disease. The scientists therefore reasoned that a protein lacking the information from exon 32 would be sufficiently active to rescue more severe cases of dysferlinopathies.
"We developed a method for skipping this exon and asked if the resulting protein was able to rescue the defects seen in cells derived from two patients. Surprisingly, we found that although the new protein was expressed at low levels, the cells had become fully capable of repairing lesions in their membrane, indicating that the therapy had worked", declares Dr Marc Bartoli.
These results established exon-skipping as a promising therapeutic strategy for the treatment of dysferlinopathies. Further pre-clinical studies in mice are currently on the way before translating these results into the development of clinical trials. Finally, similar approaches are also being developed for the treatment of other neuromuscular disorders.
Sci Rep - issue: 1 - volume: 7 - pages: 8277.
Front Genet - issue: - volume: 7 - pages: 153.
J. Thromb. Haemost. - issue: - volume: 14 - pages: 139-139.
Mov. Disord. - issue: 1 - volume: 31 - pages: 62-69.
PLoS ONE - issue: 2 - volume: 10 - pages: e0116853.
Genome Res. - issue: 12 - volume: 25 - pages: 1781-1790.
Ann. Neurol. - issue: 6 - volume: 78 - pages: 871-886.
Clin. Immunol. - issue: 2 - volume: 161 - pages: 286-290.
Neuromusc. Disord. - issue: - volume: 25 - pages: S312-S312.
Ann. Neurol. - issue: 3 - volume: 78 - pages: 387-400.
Clin. Genet. - issue: 5 - volume: 87 - pages: 488-491.
Orphanet J Rare Dis - issue: - volume: 10 - pages: 2.
Stem Cells Transl. Med. - issue: 12 - volume: 3 - pages: 1467-1472.
Stem Cells Transl Med - issue: 12 - volume: 3 - pages: 1467-1472.
Genes Dev. - issue: 22 - volume: 28 - pages: 2464-2476.
Blood - issue: 13 - volume: 123 - pages: 2116-2126.
Hum. Mol. Genet. - issue: 1 - volume: 23 - pages: 171-181.
Am. J. Hematol. - issue: 11 - volume: 88 - pages: 948-954.
Neuromusc. Disord. - issue: 9-10 - volume: 23 - pages: 824-824.
Neuromusc. Disord. - issue: 9-10 - volume: 23 - pages: 823-823.
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PLoS ONE - issue: 4 - volume: 7 - pages: e34386.
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text:
package org.apache.shardingsphere.dbdiscovery.api.config.rule;

import lombok.Getter;
import lombok.RequiredArgsConstructor;

import java.util.Properties;

/**
 * Database discovery heartbeat config.
 */
@RequiredArgsConstructor
@Getter
public final class DatabaseDiscoveryHeartBeatConfiguration {

    private final Properties props;
}

redpajama_set_name: RedPajamaGithub


Customize your ride today. BDS Motorsports LLC provides reliable car, truck, boat and all-terrain vehicle maintenance services in the Stonewall, Shreveport, & Bossier City, Louisiana area. No matter what you drive up in, you can trust the BDS Motorsports crew to make sure it's running perfectly and customize it just how you want.
We serve clients within a 75-mile radius of Stonewall, Shreveport, & Bossier City, Louisiana. Reach out to us today to book an appointment at our auto and truck maintenance shop.
Trust BDS Motorsports to help you create the ride you've always dreamed of. Drop by our auto maintenance shop in Stonewall, Shreveport, & Bossier City, Louisiana to learn more about the maintenance and customization services we provide.
Experience. Our mechanics have more than 50 years of combined experience.
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Expertise. Our full-time mechanic is a car and truck repair pro in Stonewall, Shreveport, & Bossier City, Louisiana.
BDS Motorsports opened for business in Stonewall, Shreveport, & Bossier City, LA in 2016 and services customers within a 75-mile radius. Call 318-775-4538 right now to discuss your vehicle maintenance needs with a pro who can help you get back on the road.


First Cobalt Corp. is pleased to announce it has entered into an option agreement with Cobalt One Limited to enter into a 50-50 joint venture on the Yukon cobalt extraction refinery in Cobalt, Ontario.
"The Yukon refinery and the 40 acres of permitted property can help us reduce the permitting timeline in a meaningful fashion for a future development project. We are very pleased to be partnering with Cobalt One, which was one of the first companies to identify the opportunity to revive this historic camp and is now one of the largest cobalt companies in the district."
"First Cobalt shares our vision of bulk mining in this historic camp. Their complimentary land package and strong Canadian-based management team made them logical partners for this strategic asset. I look forward to working with Trent and his team."
The refinery is located in Cobalt, Ontario, about 25 kilometres from the Company's Keeley-Frontier Project. The refinery is advantageous not only in location, but in that it is environmentally permitted to treat and process ore containing arsenic. The Yukon refinery is one of only four such permitted facilities in Canada, and the only refinery in North America with no set limits on processing or storing arsenic from feeds. This is significant given that the camp has produced silver-cobalt arsenide ores throughout its history. The other refineries permitted to treat ores containing arsenic are owned by Vale, Glencore and Sherritt International.
The refinery sits on 40.2 acres and includes a tailings management facility and two water management ponds, with both the space and environmental permits in place to expand the tailings management facility by 2-3 times the current size. The refinery is situated on a rare strategic clay-rich location within the region which is highly suitable for the existing tailings management facility and any future expansions to the facility.
The refinery was built in the mid-1990s. It was acquired in 2002 and kept on care and maintenance until 2012 when a German listed company invested several million dollars upgrading the facility as part of a brief operating agreement. On April 27, 2017 Cobalt One announced a binding agreement to purchase the refinery, which is expected to close before the end of June.
The refinery's capabilities include three separate circuits within the complex to process various secondary feeds using pressure acid-leach, solvent extraction, and Merrill-Crowe for precious metals. Primary ore feed would require the installation, or acquisition, of a mill to prepare concentrate for processing. The refinery will require upgrades to meet potential future processing demands.
The building includes the refinery, control room, labs, maintenance shop, warehouse, administrative offices and a change room. There is also a pumping station at Lake Temiskaming.
The refinery is situated in close proximity to First Cobalt's flagship asset, the Keeley-Frontier mine. Mineralization at the Keeley-Frontier mine occurs as Ag-Co-Ni-Bi-arsenides predominantly hosted in veins and stockworks known as Five-Element Vein Type deposits, making this refinery an advantageous opportunity due to its unique environmental permits to treat and process ore containing arsenic. The Keeley-Frontier exploration program began in May, and early results from surface work are anticipated in the summer, with drilling to commence about the same time.
In consideration for the option to enter a 50-50 joint venture, First Cobalt will provide immediate payment of C$750,000 to Cobalt One. First Cobalt will have until December 31, 2017 to exercise the option. On exercise, First Cobalt will be obligated to pay Cobalt One an additional C$2.25 million and pay the equivalent of 50,000,000 shares of ASX-listed Cobalt One in cash or shares of First Cobalt (approximate value of C$5.5 million).
The agreement is subject to certain conditions including, but not limited to, the receipt of all necessary regulatory and other approvals including the approval of the TSX Venture Exchange.


Fortress Westervoort, at the junction of the rivers Yssel and Rhine near Arnhem.
To the left the walls in which trenches had been dug-out. The Germans laid a deadly machinegun fire on these walls during the battle., suppressing the defenders.


text:
Q: What's the most reliable way to get the week day from a utc Date This is how I'm getting the date's week day
const date = new Date('2022-06-25T'+hour+':00:00.951Z')
const day1 = date.toLocaleString('en-US', { weekday: 'short' })

And this is how I'm constructing the date
`${date.getUTCDate()} ${date.toLocaleString('en-GB', { month: 'short' })} ${date.getUTCFullYear()}`

But apparently fails for some dates in certain hours (this is the screenshot i was sent)

If you notice the 25 of June and the 26 of June are actually Saturday and Sunday. So both date/day are not synced
After searching a bit online I found this implementation (that uses UTC function)
const weekdays = ['Sun', 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat']
const d = new Date();
let day = weekdays[d.getUTCDay()];

And I Did this little script to compare the result for 25th of June

const weekdays = ['Sun', 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat']

for (i = 0; i < 24; i++) {
   const hour = i < 10 ? '0' + i : i;
   const date = new Date('2022-06-25T'+hour+':00:00.951Z')
   const day1 = date.toLocaleString('en-US', { weekday: 'short', timeZone: 'Asia/Tbilisi' })
   const day2 = weekdays[date.getUTCDay()]
   console.log('2022-06-25 at', hour, day1, day2)
}

And you can see in the devtools that is not returning same day after 20h
2022-06-25 at 00 Sat Sat
2022-06-25 at 01 Sat Sat
2022-06-25 at 02 Sat Sat
2022-06-25 at 03 Sat Sat
2022-06-25 at 04 Sat Sat
2022-06-25 at 05 Sat Sat
2022-06-25 at 06 Sat Sat
2022-06-25 at 07 Sat Sat
2022-06-25 at 08 Sat Sat
2022-06-25 at 09 Sat Sat
2022-06-25 at 10 Sat Sat
2022-06-25 at 11 Sat Sat
2022-06-25 at 12 Sat Sat
2022-06-25 at 13 Sat Sat
2022-06-25 at 14 Sat Sat
2022-06-25 at 15 Sat Sat
2022-06-25 at 16 Sat Sat
2022-06-25 at 17 Sat Sat
2022-06-25 at 18 Sat Sat
2022-06-25 at 19 Sat Sat
2022-06-25 at 20 Sun Sat
2022-06-25 at 21 Sun Sat
2022-06-25 at 22 Sun Sat
2022-06-25 at 23 Sun Sat

So wat's the most reliable way to get the week day name?

A:

const date = new Date(Date.UTC(2022, 5, 25, 3, 23, 16, 738));
const weekday = new Intl.DateTimeFormat('en-GB', {  weekday: 'short', timeZone: 'UTC' }).format(date)

console.log(weekday)

A:
new Date('2022-06-25T'+hour+':00:00.951Z')

Is parsed as UTC (provided hour is padded to 2 digits), so far so good.

date.toLocaleString('en-US', { weekday: 'short', timeZone: 'Asia/Tbilisi' })

Gets the local day name in Asia/Tbilisi, which is +4 all year round. So for UTC times after 20:00, the local day will be the next day. To get the UTC day, use timeZone: 'UTC' in the options, e.g.

// Current UTC day
console.log(new Date().toLocaleString('en',{weekday: 'short', timeZone: 'UTC'}));

Note that UTC isn't a timezone, it's actually the opposite, it's a standard that defines a datum from which all offsets (and hence timezones) are measured.

redpajama_set_name: RedPajamaStackExchange


Susan Neumayer comes from the city's Louisville at Work team, which oversaw stimulus spending. She will replace Debbie Fox, who became interim director earlier this year, but planned to return to her job at MetroSafe in April, when Mayor Greg Fischer was expected to name a permanent director.
For more than a year, LMAS has been the target of criticism. Zelinsky became interim director after former director Gilles Meloche resigned amidst allegations of mistreatment of animals and improper behavior.
For years, reports of inhumane treatment of animals, mistreatment of employees and poor management have surrounded LMAS. Mayor Greg Fischer says that must be fixed.
Animal House is a 10 thousand square foot facility that will showcase adoptable animals under the care of Metro Animal Services.
Wayne Zelinsky, Director of Metro Animal Services, says it provides much needed space.
A State Auditor's report found numerous management problems in animal services under the tenure of former director Gilles Meloche. Amidst allegations of mismanagement and sexual harassment, Meloche resigned, effective at the end of last year. Zelinsky was promoted to fill Meloche's spot, and he says many operating procedures have been changed.
Louisville Metro Animal Services Director Gilles Meloche has announced his resignation amid accusations of mismanagement.


Below provided are some useful ideas and recommendations for students who want to write an Brainstorming Strong Ideas For Your Cause And Effect Essay.
The following professioanlly written manual that explains where to search for a reliable argumentative paper outline example. Be sure to read it.
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Introduction. In the world, college athletics grows and continues to bring large financial benefits to colleges, universities and even sponsors. This leads to many.
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Each of the scholarships listed below requires the applicant to submit an essay, video, or project in response to a specific prompt. The majority of the listings.
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This is where the topic for the persuasive speech becomes important. Texting undermines vocabulary and the mental effort that intelligent writing necessitates.
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St Mark's Breakfast Club aims to provide a secure, affordable and welcoming facility for families attending the school. We are open on a daily basis in the Main Hall and children can arrive anytime from 7.30am until the start of school.
We aim to offer a healthy choice of breakfast in a calm relaxed environment. We encourage the children to be as independent as possible in making their healthy food choices as part of a balanced diet and be responsible for clearing away their dishes. Breakfast is served up until 8.20am. Pupils can choose from a selection of breakfast items, ranging from cereals, fruit, yogurts and toast with hot toppings, like eggs or beans. There are also fresh fruit juices, milk or hot drinks to choose from.
Places are available on a first come, first served basis and will be secured at the time of payment. Parents need to book and pay in advance via the Breakfast Club during their opening hours.
After their breakfast, children can enjoy a quiet table top game or colouring activity. They are encouraged to develop their social and interactive skills with pupils from across all the year groups. On different days there are opportunities for the breakfast club children to access the ICT suite, Explore room or outdoor games on the KS1 playground.
The club is run by members of the school staff team so they will be familiar to the children. There are always at least two members of staff present. All the Breakfast Club staff are trained in First Aid and Food Hygiene and hold current CRB checks.


text:
Q: Deep learning result graph is is limited to the average area ]

You can check the Network Model and Result from the Photos.
Result datas are stuck in the "average band" and can't forecasting the exact value.
I used a 3years data to training(train, val = 8:2) and 1 year of dataset is for Test.
change Scaler, model, dense, activation function, batch size, optimizer but nothing couldn't solved graph's problem.
Does anyone know the solutions?
Appreciate any suggestions. Thx for reading!

A: I think it is not about the model that is some DATA need to be re-format that will help models understanding better.
Forgetting about the model, any model can learn in their scopes I also using only 32 cells of LSTM but the DATA need tobe easy understanding.
It is when the DATA is photo, see the minimum and maximum values you don't need to adjusting to average values but target result you expected !
Here below we using just only greater to filtered out background and remains the player and emermies !


text:
Counseling for Roma advisors
By Daniela Lazarova The main idea behind the government's plan in aid of the Romany minority is to help establish a strong network of Romany advisors who would assist the process of achieving equal opportunities for Romanies in various fields of business and education. The non-profit agency AGNES has now organized a series of educational seminars for Romany advisers across the country. The project is funded by the EU's PHARE fund and Mrs. Irena Zemankova is one of its chief organizers:
"The name of our educational programme for Roma organizations is "Education for Roma Advisors".
So what do these advisors do -what is it that you teach them?
"We would like to give them some information about how to run projects, PR , how to apply for funds and when they get grants how to use them. Basic information really ...about how to communicate with the local authorities, how to communicate among themselves - what we call team work - all things which will give them equal opportunities in competition with other organizations."
Well what has the interest been like on the part of Romanies themselves? Are they interested?
"Yes, we contacted Romany advisors in some 70 regions and nearly all of them wrote back to say they were interested in taking part in the project."
When do you expect to see results?
"The first seminar opens in March and we expect that a good Roma organization will be able to put together a sound project and cooperate well with the local authorities sometime at the beginning of 2003."
Court charges, fines activist for hoisting Czech-Romani flag from his balcony
'The younger the Czech, the more tolerant', HateFree Culture survey shows
Kher publishing house, a 'home' for Romani writing
Czech Roma returning from Britain due to Covid-19 fears, bureaucratic delays

redpajama_set_name: RedPajamaCommonCrawl


Quite the contrast from New York, which has legalized abortion up until the time of birth. Man, talk about polar opposites in the politosphere—one side has legalized it completely and the other has banned it indefinitely. Hey, look on the bright side: At least the government isn't regulating it like some kind of dictatorship.
The states can do what they want, because they have that power—but they'll be the ones standing before God on judgement day answering for it. Not you or I.
The bill passed the state House on Tuesday and the state Senate early Wednesday. Then it landed on Reynolds' desk.
New York passes New Law to give Financial Aid to Undocumented Students, what say you?


Pale yellow-green colour. The nose is fresh with sea-shells and an undertow of ripe melon-style fruit. Reasonably fresh and certainly intense in the slightly 'padded' mid-palate. Despite the padding it could only come from Chablis due to the mineral core that goes through the center. Pretty good acidity and a good length. Frankly, blind you would say a decent 1er cru or even an 'average' grand cru, such is the concentration from those old vines. Very impressive stuff and on another planet to 'basic' Chablis from the likes of Fevre.
Pale yellow. The nose is bright and forward with plenty of candied fruit; pineapple and grapefruit. Mouth-filling and quite fat, the texture is very silky/oily. Certainly very rich for a villages and a massive bargain at 86 francs for 6 bottles – provided you don't want for a little more acidity – and I do! I'll certainly look out for the 2007 though!


Airbus has been dedicated to design and manufacture aircraft that provide a better cabin experience for passengers. By investigating the use of flexible displays and flexible sensors in the cabin, Airbus plans to cooperate with Royole Technology by building a futurised, digitalised and personalised cabin to further improve the cabin environment, cabin safety and energy saving.
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text:
\section{}
\section{Introduction} \label{intro}

Relation Extraction (RE) \cite{zelenko2003kernel, mooney2006subsequence} aims to extract relations of entities from sentences, which can automate the construction of Knowledge Bases (KBs) and has potential benefits to downstream applications such as question answering \cite{sadeghi2015viske,ravichandran2002learning} and web search \cite{yan2009unsupervised}. Due to the difficulty of collecting a large amount of sentence-level annotations, most recent RE methods are based on the Distant Supervision (DS) framework \cite{mintz2009distant} which can automatically annotates adequate amounts of data by align texts with KBs: for a bag of sentences that contains two entities, if KBs has this entity pair, this bag will be labeled with the corresponding relation of the entity pair in KBs.
With the DS framework, RE can be cast as a problem of classifying a bag of sentences which contain the same query entity pair, into predefined relation classes.
However, DS framework has its own disadvantages, that is, the noise label and long-tail problems.
Figure \ref{align} shows how the label of a sentence bag is annotated and how the noise label problem is introduced by DS framework. Table \ref{longtail_num} shows the long-tail problem existing on the NYT dataset, which is a widely used DSRE dataset \cite{riedel2010modeling}.

\begin{figure}[htb]
\begin{center}
\includegraphics[width=0.8\textwidth]{DSRE_align.eps}
\caption{\label{align} An example of how the training instances are generated through distant supervision. \textit{S2} and \textit{S3} are wrong labeled with the relation ``\textit{/business/company/founders}'' since they do not express the relation.}
\end{center}
\end{figure}

\begin{table}[htb]
\caption{\label{longtail_num}The sample numbers of different relation classes in the NYT training set, which is pretty class-imbalanced: the samples of half relation classes is less than 100.}
\centering
\begin{tabular}{cc}
\hline
realtion class & number of class samples\tabularnewline
\hline
\textit{/location/location/contains} & 75969\tabularnewline
\textit{/people/person/nationality} & 11446\tabularnewline
\textit{/business/person/company} & 7987\tabularnewline
\textit{/location/us\_state/capital} & 798\tabularnewline
\textit{/business/company/place\_founded} & 677\tabularnewline
... & ...\tabularnewline
\textit{/location/it\_region/capital} & 22\tabularnewline
\textit{/business/company/locations} & 19\tabularnewline
\textit{/broadcast/content/location} & 8\tabularnewline
\textit{/location/jp\_prefecture/capital} & 2\tabularnewline
\textit{/business/shopping\_center/owner} & 1\tabularnewline
\hline
\end{tabular}
\end{table}

In the existing works, many efforts \cite{riedel2010modeling,surdeanu2012multi,zeng2015distant} have been devoted to reducing the effect of the noise label problem by recognizing the valid sentences. To be specific, the attention mechanism \cite{lin2016neural}, its variants \cite{du2018multi,yuan2019cross,huang2019self} and other methods \cite{liu2017soft, wu2017adversarial} are proposed to address the noise label problem and achieve new state-of-the-arts.
However, these works consider relations in isolation and ignore the connections between relation classes.
To take advantage of those connections,
\cite{ye2017jointly} utilizes class ties in relations and incorporate it with a pairwise ranking framework.
\cite{han2018hierarchical} utilizes the relation hierarchies in KBs and proposes a hierarchical attention scheme to further enhance the relation representations.
Based on \cite{han2018hierarchical}, \cite{katt_naacl19} integrates the Knowledge Graph Embedding into relation extraction model and improves the long-tail class accuracy.
They both transfer the well-trained features to the long-tail classes by connecting relations according to the KB hierarchies.
Besides, there are also works utilize external information (e.g., entity type information \cite{liu2014exploring,vashishth2018reside}) to enhance DSRE. They consider the entity type information as input features, which can be classified along with the sentence features. Nevertheless, the way these approaches \cite{liu2014exploring,vashishth2018reside} utilizing the entity type information is inefficient, since they ignore the connections between relation classes and query entities.

\begin{figure}[htb]
\begin{center}
\includegraphics[width=0.9\textwidth]{styleshift.eps}
\caption{\label{subclass} An example of the style shift problem in DSRE. The keywords that convey this relation are in red font.
}
\end{center}
\end{figure}

This paper studies the DSRE problem by re-examining the relation definitions in the existing methods. On one hand, the class definitions may not be fine-grained enough, since the style that a sentence expresses the entity relation may vary for different query entity pairs. For example, given two sentences in Figure \ref{subclass}, they both express the same relation. However, the keywords that convey this relation are quite different due to the difference in query entity types, i.e., (\textit{Person, Organization}) VS. (\textit{Person, News agency}).
It seems that we need to further consider the style shift problem of each relation class concerning the entity types.

On the other hand, some relation definitions may be fine-grained since many relations are semantically related and their representations may be similar in the feature space. For example:
\begin{itemize}
\item ``\textit{/people/person/place\_of\_birth}'' and ``\textit{/people/person/place\_lived}''\\ both indicate someone appears somewhere at a certain time.
\end{itemize}
Moreover, the head and tail entities mentioned in these two relation classes are with the ``\textit{/person}'' and ``\textit{/location}'' types, respectively. That is, they can be trained well cross different relation bags.
It can be expected that based on this intuition, the well-trained entity type information can be utilized to further enhance the vanilla relation representations. That especially benefits the long-tail problem.

To unify these two arguments, we propose to use a dynamic neural network in which the class-dependent parameters (i.e., attention and classifier) can be dynamically determined by the query entity type information.
By doing so, we can make our prediction model adaptive to the query entity types which can naturally deal with the style shift problem. Also, the long-tail relation representations can be derived from those entity type information, and it can further address the long-tail problem. By utilizing the type information this way, we can  tackle DSRE more efficiently.

Specifically, to dynamically generate class-dependent parameters (i.e., attention and classifier) for our model, we develop a novel dynamic parameter generation module. Such a module generates network parameters in two steps: (1) The corresponding entity types of one bag are firstly converted to type embeddings. Then, a relation-aware attention mechanism\footnote{One entity may have multiple entity types. Therefore, the information of the entity types needs to be aggregated effectively.} is proposed to aggregate the important information of them for a specific relation class.
That is, different relations may correspond to different entity types.
(2) The aggregated information of entity types is incorporated with the vanilla relation representations, and it is further transformed into the dynamic class representations by nonlinear mapping.

We conduct experiments on a widely used large-scale DSRE benchmark dataset, and the experimental results demonstrate the superior performance of the proposed method. It is validated that the dynamic network design is beneficial for handling both style shift and long-tail problems in DSRE.

In summary, our main contributions of this work are as follows:
\begin{itemize}
    \item We first utilize the class relationship with entity types for improving the performance of DSRE.
    \item We propose a novel dynamic parameter generator to build a dynamic neural network whose parameters are determined by the query entity types and relation classes.
	 \item We propose a novel relation-aware attention over entity types to aggregate the discriminative information in the entity types.
    \item Our experiments on a widely used benchmark show that our method gives a new state-of-the-art result.
\end{itemize}

\section{Related Works}

\subsection{Hand-crafted Feature Based Methods}

In its early years, most of the DSRE methods are based on the hand-crafted features \cite{mintz2009distant,riedel2010modeling,hoffmann2011knowledge}, e.g., POS tags, named entity tags, and dependency paths.
\cite{mintz2009distant} assumes that sentences containing the same entity pair, all express the same relation. However, this assumption does not always hold.
To relax this assumption, \cite{riedel2010modeling} assumes that if two entities are held in a relation, at least one sentence mentioning these entities may express such relation. Then, they employ the multi-instance learning (MIL) paradigm to support this assumption.
Later, since different relational triplets may have overlaps in a sentence and MIL can not address this problem, \cite{hoffmann2011knowledge,surdeanu2012multi} extend MIL to multi-instance multi-label paradigm to handle this problem. However, the hand-crafted features are not sufficiently robust, as well as lead to the error propagation problem.

\subsection{Deep-feature Based Methods}

In recent years, researchers turn to apply deep learning to DSRE due to its promising performance and generalization ability in various NLP applications. Many methods \cite{zeng2015distant,lin2016neural,ji2017distant} are under the MIL paradigm framework aiming to denoise the wrong label generated by DS.
\cite{zeng2015distant} proposes a piecewise convolutional neural network, and utilizes MIL to select one sentence in a bag which can well express the relation between the entity pair within such a sentence.
However, they omit useful information in other sentences, which are also useful for expressing such a relation. To solve this problem, \cite{lin2016neural} introduces a selective attention mechanism to capture more useful information in other sentences.
Afterwards, its variants \cite{du2018multi,yuan2019cross,huang2019self,ye-ling-2019-distant} are proposed,
where \cite{du2018multi} introduces a multi-lingual neural relation extraction framework to better utilize the information from various languages;
\cite{ye-ling-2019-distant} considers both intra-bag and inter-bag attentions in order to deal with the noise at sentence-level and bag-level respectively;
\cite{yuan2019cross} extends the selective attention to cross-relation cross-bag selective attention and trains the model more noise-robust;
\cite{huang2019self} enhances convolutional neural network with the self attention mechanism to learn better sentence representations;

Besides, \cite{zheng2017joint} proposes a tagging based method that jointly extracts entities and relations.
\cite{ijcai2018-620} converts the joint task into a directed graph by designing a graph scheme and propose a transition-based approach to generate the directed graph.
\cite{liu2018neural} reduces the noise via sub-tree parse and utilizes transfer learning to improve the performance of DSRE.
\cite{su2018globalR} proposes to embed textual relations with global statistics of relations
\cite{han2018neural} proposes a joint representation learning framework on knowledge graph completion and relation extraction from text.
\cite{he2018see} proposes to learn syntax-aware entity embedding for DSRE.
\cite{jia2019arnor} proposes an attention regularization method to further reduce the noise in the dataset. \cite{zhengetal2019diag} proposes a neural pattern diagnosis framework, which can automatically summarize and refine high-quality relational patterns from noisy data.
Other learning strategies, like adversarial training \cite{wu2017adversarial,wang2018adversarial,qin-etal-2018-dsgan}, capsule network \cite{zhangetal2018attention,zhang2019multi}, and reinforcement learning \cite{feng2018reinforcement,takanobu2019hierarchical} are also applied to DSRE to further improve its performance.

Recently, due to the powerful capabilities of the pre-training language model, \cite{altetal2019fine} utilizes GPT \cite{radford2018improving} for DSRE and predicts a larger set of distinct relation types with high confidence. In another way, \cite{chenetal2019global} studies how to learn a general-purpose embedding of textual relations and further improves the DSRE performance.

\subsection{Methods Incorporating External Information}

Recently, other useful external information is identified to be beneficial for DSRE, e.g., KB information.
\cite{ji2017distant} utilizes entity descriptions for DSRE, which can provide rich background information of entities, and help recognize relations in DSRE.
\cite{liu2014exploring} explores fine-grained entity type constraints for DSRE.
\cite{liu2017heterogeneous} utilizes annotations from the heterogeneous information sources, e.g., knowledge base and domain heuristics, and conduct relation extractor learning.
\cite{vashishth2018reside} uses a set of side information, e.g., entity type, and relation alias, to boost DSRE performance.
\cite{wang-etal-2018-label-free} proposes a label-free distant supervision method, and only uses the prior knowledge derived from the KG to supervise the learning of the classifier softly.
\cite{lei2018cooperative} leverages the corpus-based and KG-based information, and use logic rules on the entity type level.
\cite{han2018hierarchical} proposes a coarse-to-fine grained attention scheme by hierarchical relation structures in KB.
Based on \cite{han2018hierarchical}, \cite{katt_naacl19} proposes a knowledge-aware attention scheme using Knowledge Graph embedding (KGE).
Besides, \cite{Beltagy2019Comb} combines the distant supervision data with additional directly-supervised data to train a model for identifying valid sentences.
\cite{hu2019improving} proposes a multi-layer attention-based model to improve DSRE with joint label embedding, which is obtained from entity descriptions and KG.

However, all the above works ignore the style shift problem, whereas DNNRE uses the entity type information to address it and further improve DSRE performance.
Note that there are also previous works using entity types in their models \cite{vashishth2018reside}. However, they are quite different from us: we utilize entity types to dynamically generate the parameters in our model for addressing the style shift and long-tail problem, whereas previous works just use entity types as input features.

\begin{figure}[htb]
\begin{center}
\includegraphics[width=1.\textwidth]{KBS_over_archi.eps}
\caption{\label{main_framework}Overview of \textbf{DNNRE}. The sentence encoder is in the top left. The dynamic Neural network parts are in the top right, in which the attention (Dynamic Attention) and classifier (Dynamic Classifier) are generated by the dynamic parameter generator in the bottom. Note that the fully connected layers (FC) which map $\overline{w}_i$ to dynamic attention and classifier modules \textbf{do not} share parameters.}
\end{center}
\end{figure}

\section{Methodology}

The primary idea of the proposed method is to build a network with \textbf{DYNAMIC} weights, that is, parts of the network parameters will be dynamically generated by the entity types and relation classes. This is in contrast to the traditional methods which use \textbf{STATIC} models for which the model parameters will be fixed during testing. Formally, the class-dependent parameters $\boldsymbol{\lambda}$ of the proposed network can be dynamically generated by the following function:
\begin{equation}\label{eq:overall_idea}
    \boldsymbol{\lambda} = \phi(\mathbf{T}_{e}, \mathbf{w}_i),
\end{equation}
where $\mathbf{T}_{e}$ (i.e., $\mathbf{T}_e^{head}$ and $\mathbf{T}_e^{tail}$) is the entity types,
and $\mathbf{w}_{i}$ is the vanilla relation representation of $i$-th class in the attention or classifier modules.
The function $\phi$ is called \textbf{dynamic parameter generator} which transfer $\mathbf{T}_{e}$ and $\mathbf{w}_i$ into the network parameters $\boldsymbol{\lambda}$.

Since $\mathbf{T}_{e}$ is a variable of the query entity types, the generated network parameters will be online adapted at the test stage, which offers a solution to compensate for the style shift and long-tail problems.

\subsection{Overall Architecture}

The overall architecture of the proposed DNNRE is illustrated in Figure \ref{main_framework}. In the top right, the \textbf{Sentence Encoder} encodes a bag of sentences into sentence representations.
Meanwhile, the \textbf{Entity Type Mapping} maps the entity pair to $\mathbf{T}_{e}$ for enhancing the relation representations.
Then, this information will be utilized to generate the dynamic parameters by the \textbf{Dynamic Parameter Generator} (bottom). Finally, the dynamic parameters will build the \textbf{Dynamic Neural Network} (i.e., dynamic attention and classifier) in the top right. The \textbf{Dynamic Attention} aggregates the sentence representations into a bag representation, which is feed into the \textbf{Dynamic Classifier} to predict its corresponding relation class.

In the remaining of this section, the contents are organized as follows:

\begin{itemize}
    \item Firstly, the Sentence Encoder will be introduced briefly in subsection \ref{encoder}.
    \item Then, the Entity Type Mapping (i.e., $\mathbf{T}_{e}$) will be introduced in subsection \ref{input_des}.
    \item The Dynamic Parameter Generator will be elaborated in subsection \ref{generator}.
    \item Finally, the Dynamic Neural Network  is introduced in subsection \ref{dyna_parts}.
\end{itemize}

\subsection{Sentence Encoder}\label{encoder}

In the framework of DSRE, the input of the network is a bag of sentences. Similar as most DSRE methods \cite{zeng2015distant,lin2016neural}, we first convert each sentence $\mathscr{\mathscr{\mathcal{S}_i}}=\left\{ w_{1},w_{2},...,w_{|s|}\right\}$ into a fixed-length vector $\mathbf{s}_i$ by using a sentence encoder. In this work, we use PCNN \cite{zeng2015distant} to fulfill this task. The sentence encoder has three layers: input representation layer, encoding layer and pooling layer, which are introduced as follows:

\subsubsection{Input Representation Layer}

The input representation layer converts each word in a sentence by the word embedding and the position embedding.

The word embedding is used to represent each word token $i$ of $w_{i}$ by a pre-trained word embedding vector $\mathbf{v}_{i}$, which is trained on NYT corpus by the word2vec tool\footnote{https://code.google.com/p/word2vec/} \cite{mikolov2013efficient}.

The position embedding consists of two fixed dimension vectors for representing the relative positions between $w_{i}$ and entity pair. We concatenate position embedding $\mathbf{p}_{i1},\mathbf{p}_{i2}$ to the word representation.

\subsubsection{Encoding Layer}
The word representations $\mathbf{x}_{i}=\left[\mathbf{v}_{i};\mathbf{p}_{i1};\mathbf{p}_{i2}\right]\in \mathbb{R}^{d_{i}}$ ($d_{i}=d_{w}+2\times d_{p}$) are fed into the encoding layer. $m$ convolution kernels $\mathbf{K}=\left\{ \mathbf{k}_{1},...,\mathbf{k}_{m}\right\} \in \mathbb{R}^{n_{w}\times d_{i}}$ slide over the input to capture features in the ${n_{w}}$-gram:
\begin{equation}\label{eq1}
    \mathbf{h}_{i}=\mathbf{k}_{i}*\mathbf{x}_{j-n+1:j}\quad1\leq i\leq m,
\end{equation}
where $\mathbf{x}_{j-n+1:j}$ means the word representation from index $j-n+1$ to $j$. Afterwards, we can obtain $\mathbf{H} = \left\{ \mathbf{h}_{1},...\mathbf{h}_{m}\right\}$.

\subsubsection{Pooling Layer}
After this convolution operation, a piecewise max-pooling is adopted to aggregate word-level information. Supposed $\mathbf{h}_{i}$ is split into $\left\{\mathbf{h}_{i1},\mathbf{h}_{i2},\mathbf{h}_{i3}\right\}$ by the entity positions, this pooling method is described as below:
\begin{equation}\label{eq2}
    \mathbf{q}_{i}=[maxpool(\mathbf{h}_{ij})]\quad j=1,2,3.
\end{equation}

Then we obtain $\mathbf{Q} \in \mathbb{R}^{m\times 3}$, and $\mathbf{Q}$ is flattened to a vector and translate it into the sentence embedding $\mathbf{s} \in \mathbb{R}^{d_{s}}$ by a non-linear layer.

\newcommand{\tabincell}[2]{\begin{tabular}{@{}#1@{}}#2\end{tabular}}
\begin{table}[htb]
\caption{\label{typeset}The entity types used in our model. The left column lists coarse-grained entity types and the right column lists fine-grained entity types corresponding to the left. We put the hard-categorized entity types in the bottom. Note that we only show part of the 112 entity types due to the space constraint.}
\begin{center}
\begin{tabular}{c|c}
\hline
Coarse-grained&Fine-grained\tabularnewline
\hline
person & \tabincell{c}{doctor engineer architect coach actorreligious\_leader\\monarch terrorist  artist musician athlete director}\tabularnewline
\hline
organization & \tabincell{c}{terrorist\_organization airline government company\\government\_agency educational\_institution military\\fraternity\_sorority political\_party news\_agency}\tabularnewline
\hline
location & \tabincell{c}{body\_of\_water city island country park astral\_body\\mountain county glacier province bridge road}\tabularnewline
\hline
product & \tabincell{c}{camera engine mobile\_phone airplane software  weapon\\computer ship game spacecraft instrument train car\\}\tabularnewline
\hline
art & \tabincell{c}{written\_work film newspaper play music}\tabularnewline
\hline
event & \tabincell{c}{military\_conflict attack natural\_disaster election\\sports\_event protest terrorist\_attack}\tabularnewline
\hline
building & \tabincell{c}{airport dam hospital hotel library power\_station\\ restaurant sports\_facility theater}\tabularnewline
\hline
\multicolumn{2}{c}{\tabincell{c}{chemical\_thing website color biological\_thing award time disease god\\broadcast\_network medical\_treatment broadcast\_program title drug\\educational\_degree tv\_channel symptom currency law algorithm\\ethnicity body\_part language living\_thing food animal religion\\programming\_language stock\_exchange transit\_system transit\_line}}\tabularnewline
\hline
\end{tabular}
\end{center}
\end{table}

\subsection{External Information Acquisition} \label{input_des}

The external information of entity types has been proved to be useful for the DSRE task as additional input features \cite{liu2014exploring,vashishth2018reside}. Unlike those existing works, we use the entity type information $\mathbf{T}_{e}$ to dynamically determine the network parameters. The representation of it is shown as follows.

We first obtain the entity types from KB and further mapping to the types predefined by FIGER \cite{ling2012fine}, which are shown in Table \ref{typeset}.
Then, we create an embedding vector for each entity type. Note that in practice, one entity may correspond to multiple entity types, in such a case, we propose a relation-aware attention to selectively aggregate the important information in these entity type embedding and obtain an aggregated vector to represent them. Besides, some entities may correspond to no entity type, we then use an ``\textit{UNK}'' token to represent their entity type.

Note that if the entity type information can not be directly obtained from KBs, we can also use the entity typing methods \cite{ling2012fine,xin2018improving} to obtain this information. In this paper, we assume the entity type information is easy to access and directly obtain it from KBs.

\subsection{Dynamic Parameter Generator} \label{generator}
In the following, the implementation of the dynamic parameter generator $\phi(\mathbf{T}_{e}, \mathbf{w}_i)$ is elaborated in detail. In our design, it achieves parameter generation through two stages:
(1) In \textit{stage 1}, since one entity may correspond to several entity types, we aggregate the optimal information in these entity types which is most important to enhance the relation representations. (2) In \textit{stage 2}, the aggregated entity type information is further utilized to generate the dynamic parameters.

\subsubsection{Stage 1: Type Information Aggregation}
The first stage is to aggregate the type information $\mathbf{T}_{e}$ (i.e., $\mathbf{T}_e^{head}$ and $\mathbf{T}_e^{tail}$) into two embedding vectors $\mathbf{t}_e^{h}$ and $\mathbf{t}_e^{t}$ with the dimension of $d_n$, which represents the aggregated head entity and tail entity type information, respectively.

The reason to selectively aggregate such type information is that different relation classes may correspond to different entity types. For example: the location ``Sacramento'' can be mentioned by relations ``\textit{/location/us\_state/capital}'' and ``\textit{/people/person/place\_of\_birth}''. The former may attend more to its type ``\textit{/city}'', and the latter may attend more to ``\textit{/location}''.

This step is achieved by a relation-aware attention over the entity type embedding. This attention mechanism enables the model to aggregate the most discriminative information related to a specific relation class. The attention weights over the type embedding are calculated as follows:
\begin{align}\label{eq3}
    &\alpha_{i} = \mathbf{t}_{i}^h\mathbf{W}_{t}{\mathbf{w}}_k^T, \nonumber \\
	&\mathbf{t}_e^{h} = \sum_{i=1}^{n_{s}}\frac{\exp(\alpha_{i})}{\sum_{j=1}^{n_s} \exp(\alpha_{j}) }\mathbf{t}_{i}^h,
\end{align}
where $\mathbf{W}_{t} \in \mathbb{R}^{{d_{t}}\times{d_{r}}}$ are learnable parameters. $\mathbf{w}_{k} \in \mathbb{R}^{d_{r}}$ is the static representation for the $i$-th class in the vanilla attention or classifier. $\mathbf{t}_{i}^h$ is one of the type embedding vectors in $\mathbf{T_e^{head}}$, which is a set of entity type embeddings $\left\{ \mathbf{t}_{1}^h,...,\mathbf{t}_{|T_{head}|}^{h}\right\}$. $\mathbf{t}_e^{t}$ is also aggregated from $\mathbf{T_e^{tail}}$ by the same aggregation strategy shown above.

Note that we also investigate another two aggregation strategies (i.e., average pooling and max pooling) to compare with the proposed relation-aware attention over the entity types. One can refer to subsection \ref{aggregation_strategy} for a detailed discussion.

\subsubsection{Stage 2: Dynamic Parameters Generation}

After we have aggregated the entity type information and obtained $\mathbf{t}_e^{h}$ and $\mathbf{t}_e^{t}$, they are further utilized to generate the dynamic class parameters by the following transformation:
\begin{equation}
    \overline{\mathbf{w}}_{k}=f_{d}(\mathbf{w}_{k}+f_{t}([\mathbf{t}_e^{h},\mathbf{t}_e^{t}])),
\end{equation}
where the term $f_{t}([\mathbf{t}_e^{h},\mathbf{t}_e^{t}])$ can be considered as a dynamic component generated from the information of the head and tail entity types. $f_{d}(\cdot)$ and  $f_{t}(\cdot)$ both denote the fully connected layers, and two layer fully connected module is applied in our design. Besides, $[\cdot, \cdot]$ denotes concatenation of two vectors.

After the transformation, $\overline{\mathbf{w}}_{k}$ can be utilized as the network parameters of the $k$-th class in the attention and the classifier. In our work, the dynamic parameters of the attention and classifier used two different $f_d(\cdot)$. That is, the Dynamic Parameter Generator for the attention and classifier share its parameters except for the fully connected module $f_d(\cdot)$.
For clarity, we denote them as $\phi_a(\mathbf{T}_{e}, \mathbf{r}_k) \in \mathbb{R}^{d_r}$ and $\phi_c(\mathbf{T}_{e}, \mathbf{w}_k) \in \mathbb{R}^{d_r}$, where $\mathbf{r}_k, \mathbf{w}_k$ are the vanilla static parameters for the $k$-th class in the attention and classifier, respectively. $d_r$ is the dimension of the parameters for each class.

\subsection{Dynamic Neural Network} \label{dyna_parts}

After the sentences being encoded into vector representations, the next operation is to aggregate them into a bag representation by attention mechanism. Finally, the bag representation is fed into a classifier.

Attention and classifier both measure the similarity between features and relations, at sentence and bag level, respectively.  In that sense, the dynamic parameter generator can enhance both of them, which will be introduced as dynamic attention and dynamic classifier module in the following parts.

\subsubsection{Dynamic Attention}

Given $n_{s}$ sentences in a bag, their corresponding features are extracted by PCNN as $\mathbf{S}=\{\mathbf{s}_{1},...,\mathbf{s}_{n_s}\}$, it is a common practise to use the attention mechanism to generate $n_{s}$ weights to selectively attend the most relevant sentence. Then, the sentence features are aggregated to a fixed-length vector representation for a bag.

In our work, the attention parameters will be generated by the dynamic parameter generator, and the attention weights are calculated as follows:
\begin{align}\label{eq6}
    &\overline{\mathbf{r}}_k = \phi_a(\mathbf{T}_{e}, \mathbf{r}_k), \nonumber \\
    &\alpha_{i} = \mathbf{s}_{i}\overline{\mathbf{r}}_k, \nonumber \\
    &\mathbf{z}_k = \sum_{i=1}^{n_{s}}\frac{\exp(\alpha_{i})}{\sum_{j=1}^{n_s} \exp(\alpha_{j}) }\mathbf{s}_{i},
\end{align}
where $\overline{\mathbf{r}}_k$ is dynamic attention parameters for the $k$-th class. $\mathbf{s}_{i}$ is the sentence feature. Note that we run the dynamic attention $n$ times to obtain $n$ aggregation results, i.e., $[\mathbf{z}_1,...,\mathbf{z}_n]$.

\subsubsection{Dynamic Classifier}

Each result $\mathbf{z}_k$ is classified by its corresponding classifier. In other words, the decision value for the $k$-th class is
\begin{align}
    &\overline{\mathbf{w}}_k = \phi_c(\mathbf{T}_{e}, \mathbf{w}_k), \nonumber \\
    &v = \overline{\mathbf{w}}_k\mathbf{z}_k + b_k,
\end{align}where $\overline{\mathbf{w}}_k$ is dynamic classifier parameters for the $k$-th class.
and $b_k$ is a bias term. Note that at the test stage, we do not know the ground-truth relation category $k$, thus we run the dynamic attention and the dynamic classifier $n$ times with a hypothesis $k$ each time. Each run will produce a posterior probability for the $k$-th class, and this result will be used for prediction and evaluation. The same operation has also been used in \cite{lin2016neural}.

\section{Experimental Results}

In this section, we first describe the dataset and evaluation criteria. Then, we show our implementation details. Finally, we report our results compared with other existing methods.

\subsection{Dataset}

We evaluate our method on a widely used dataset\footnote{http://iesl.cs.umass.edu/riedel/ecml/}, NYT, which is developed by \cite{riedel2010modeling}. The NYT dataset is generated by aligning Freebase \cite{bollacker2008freebase} relation facts with the New York Times corpus. The entities in sentences are recognized by the Stanford named entity tagger \cite{manning-etal-2014-stanford} and further matched the corresponding Freebase entities. The NYT dataset has been widely used as a benchmark in the existing literature \cite{hoffmann2011knowledge,surdeanu2012multi,zeng2015distant} etc. The sentences from the year 2005-2006 in New York Times corpus are used to generate the training set and the sentences from the year 2007 are used as the testing set. There are 53 relation classes in the dataset, including NA which means no relation between two entities.

\subsection{Evaluation Criteria}

Following the existing works \cite{mintz2009distant,lin2016neural}, we use a held-out evaluation method to evaluate the models. The held-out evaluation method compares the predicted relation classes with the ground truth to evaluate the corresponding method. The Precision-Recall (PR) curves and the top-N precision (P@N) will be reported for analysis. Moreover, to further evaluate our method on long-tail relations, we follow \cite{han2018hierarchical,katt_naacl19} and apply Hits@K metrics. In Addition, in the ablation study, we use AUC for quantitative analysis.

\begin{table}[htb]
\caption{\label{parameter_set} Hyper-parameter settings used in our model.}
\begin{center}
\begin{tabular}{lc}
\hline
\bf Parameter Name & \bf Value \\
\hline
Word dim. $d_{w}$ & 50  \\
Position dim. $d_{p}$ & 5 \\
Type dim. $d_{t}$ & 50 \\
Sentence emb. dim. $d_{s}$ & 690 \\
Relation emb. dim. $d_{r}$ & 690 \\
Window size $w$ & 3 \\
Batch size $B$ & 160 \\
Learning rate ${\lambda}_{lr}$ & 1.0 \\
Dropout probability $p_d$ & 0.5 \\
L2-regularization ${\lambda}_{L_2}$ & 1e-5 \\
\hline
\end{tabular}
\end{center}
\end{table}

\subsection{Implementation Details}

We use the same hyper-parameter settings in PCNN \cite{zeng2015distant}. The dimension of entity type and relation tuple element embedding are both set to 50. GCN layers are set to 2. The cross-entropy loss function is applied to train our model. The Adadelta optimizer \cite{zeiler2012adadelta} with its default parameters is used as the optimizer. Moreover, the dropout strategy \cite{srivastava2014dropout} is used at the classification layer, and L2-regularization is also used to prevent the model training from over-fitting. We implement the network based on PyTorch. Table \ref{parameter_set} reports all the hyper-parameter settings.

\begin{figure}[tb]
\begin{center}
\includegraphics[width=.8\textwidth]{overall_performance.eps}
\caption{\label{allpr}Performance comparison of proposed models with other methods.}
\end{center}
\end{figure}

\subsection{Overall Evaluation Results}

To evaluate the performance of \textbf{DNNRE}, we compare it against several existing hand-crafted feature based and deep-feature based methods, which are as follows:

\begin{itemize}
\item \textbf{Mintz}: \cite{mintz2009distant} represents a traditional multi-class logistic regression DSRE model.
\item \textbf{MultiR}: \cite{hoffmann2011knowledge} proposes a probabilistic graphical model for multi-instance learning.
\item \textbf{MIML}: \cite{surdeanu2012multi} applies a graphical model which jointly models multiple instances and multiple labels.
\item \textbf{PCNN+MIL}: \cite{zeng2015distant} is a convolutional neural network (CNN) adopting a piecewise max-pooling method for sentence representation.
\item \textbf{PCNN+ATT}: \cite{lin2016neural} uses selective attention over instances to aggregate sentence embeddings to a bag-level embedding, which can utilize the information in other sentences.
\item \textbf{RESIDE}: \cite{vashishth2018reside} utilizes side information (i.e., relation alias and entity type information) and GCN is used to encode syntactic information of instances for boosting the DSRE performance.
\item \textbf{PCNNs+WN} \cite{yuan2019distant} proposes to use a linear attenuation simulation to weight the word embeddings and a non-independent and identically distributed relevance hypothesis is used to capture the relevance of sentences in the bag.
\end{itemize}

From the PR curves in Figure \ref{allpr}, it can be observed that DNNRE achieves superior performance compared with the state-of-the-arts. The precision value of DNNRE outperforms others under almost all recall values. Especially, when recall ranges from 0.10 to 0.40, there is a consistent margin between DNNRE and other methods. By cross-referencing the P@N results in Table \ref{Precision@N}, it is clear that our method achieves a significant improvement over the comparing methods.

To highlight, compared with a recent method RESIDE, which utilizes side information (e.g., entity type and relation alias).
DNNRE achieves higher precision by a large margin over all recall values in PR curves and attains an improvement pf 4.2\% in P@N on average. That demonstrates that our method of using the entity type information is more effective.

The performance of DNNRE indicates that the design of the dynamic network can take advantage of the class relation with corresponding entity types. It can dynamically adapt its parameters to represent the relations more accurately. A case study is reported for evaluating the effectiveness of DNNRE for the style shift problem caused by keyword variation in subsection \ref{case_study}.

\begin{table}[tb]
\caption{\label{Precision@N} P@N comparison of proposed models with other methods. The best results are in bold font.}
\begin{center}
\begin{tabular}{cccccc}
\hline
P@N & PCNN+MIL & PCNN+ATT & RESIDE & PCNNs+WN & DNNRE \\
\hline
P@100 & 72.3 & 76.2 & 84.0 & 83.0 & \bf85.0 \\

P@200 & 69.7 & 73.1 & 78.5 & 82.0 & \bf 83.0 \\

P@300 & 64.1 & 67.4 & 75.6 & 80.3 & \bf 82.7 \\

Mean & 68.7 & 72.2 & 79.4 & 81.8 & \bf 83.6 \\
\hline
\end{tabular}
\end{center}
\end{table}

\begin{table}[tb]
\caption{\label{HitsK}Accuracy (\%) of Hits@K on relations with training instances fewer than 100/200.}
\begin{center}
\begin{tabular}{ccccccc}
\hline
\# Instances & \multicolumn{3}{c}{$<100$} & \multicolumn{3}{c}{$<200$}\tabularnewline
Hits@K  & \multicolumn{1}{c}{10} & \multicolumn{1}{c}{15} & 20 & \multicolumn{1}{c}{10} & \multicolumn{1}{c}{15} & 20\tabularnewline
\hline
PCNN+ATT & $<5.0$ & 7.4 & 40.7 & 17.2 & 24.2 & 51.5\tabularnewline
PCNN+HATT & 29.6 & 51.9 & 61.1 & 41.4 & 60.6 & 68.2\tabularnewline
PCNN+KATT & 35.3 & \textbf{62.4} & 65.1 & 43.2 & 61.3 & 69.2\tabularnewline
DNNRE & \textbf{57.6} & 62.1 & \textbf{66.7} & \textbf{64.1} & \textbf{68.0} & \textbf{71.8}\tabularnewline
\hline
\end{tabular}{\scriptsize\par}
\end{center}
\end{table}

\subsection{Evaluation for Long-tail Relations}

We also evaluate the performance of DNNRE on \textbf{Long-tail Relations} by following the protocol of \cite{han2018hierarchical,katt_naacl19}: (1) A subset of the test dataset in which all the relations have fewer than 100/200 training instances is selected. (2) Hits@K with $K=\left\{10,15,20\right\}$ metrics is used as an evaluation metric, which measures the likelihood of true relation falls into the first K candidate relations recommended by the model.

In Table \ref{HitsK}, it is observed that our method outperforms \textbf{PCNN+ATT} \cite{lin2016neural}, \textbf{PCNN+HATT} \cite{han2018hierarchical} and \textbf{PCNN+KATT} \cite{katt_naacl19} in most of the Hits@K. In particular, we observe that DNNRE achieve 57.6\% and 64.1\% accuracies for Hits@10. That is, more than half of true classes of the long-tail samples can be predicted into the first $10$ candidate relation recommended by DNNRE, which outperforms other works significantly at least 20\% absolute improvement.
This demonstrates that DNNRE can substantially boost the performance of long-tail relation classes.

\section{Analysis and Discussion}
In this section, we first conduct the ablation study to analyze the effects of each dynamic component of DNNRE (Subsection {\ref{ablation_study_over}).
Secondly, we compare our relation-aware attention over entity types with other aggregation strategies, i.e., average pooling and max pooling (Subsection {\ref{aggregation_strategy}).
Then, we investigate the effects of the different granularities of the entity types on the capability of the generated dynamic parameters (Subsection \ref{diff_grained}).
Finally, a case study is given to demonstrate the effectiveness of DNNRE (Subsection \ref{case_study}).

\begin{figure}[htb]
\centering
\includegraphics[width=.8\linewidth]{ablation_pr.eps}
\caption{Ablation study of different dynamic components of DNNRE.}
\label{ablation}
\end{figure}

\subsection{Ablation Study} \label{ablation_study_over}

In this subsection, we conduct ablation studies to validate the effect of each component of DNNRE (i.e., the dynamic attention and dynamic classifier).
The applied evaluation metrics we use are the PR curve and AUC. Note that since some bags in the testing set are noisy, we use AUC (recall $<0.4$) to focus on the high confidence bags in the low-recall region.

In Figure \ref{ablation}, we report different ablated version of DNNRE, which is described as follows:
\begin{itemize}
\item \textbf{w/o dynatt} denotes a variant by removing the dynamic attention and only use the vanilla static attention parameters.
\item \textbf{w/o dyncls} denotes a variant by removing the dynamic classifier and only use the vanilla static classifier parameters.
\end{itemize}
Merely using dynamic attention (\textbf{w/o dyncls}) or dynamic classifier (\textbf{w/o dynatt}) can boost the AUC to around 0.264 and 0.290, respectively. Particularly, when the dynamic classifier is removed, the performance drops to 0.264. As a result, it indicates that the dynamic classifier is more important for the DNNRE.
Besides, when these two dynamic components are both utilized in our model, the DNNRE achieves an AUC of 0.303, which means that the dynamic attention and classifier are complementary to each other. The results demonstrate that each dynamic module contributes to the superior performance of DNNRE, that is, both the dynamic design of the attention and classifier are beneficial for relation recognition.

\begin{figure}[htb]
\begin{center}
\includegraphics[width=.8\textwidth]{aggregation_strategy_pr.eps}
\caption{\label{aggregation_pr}Performance comparison of different aggregation strategies.}
\end{center}
\end{figure}

\subsection{Effects of Different Aggregation Strategies} \label{aggregation_strategy}

In this subsection, we investigate the effects of different aggregation strategies.
Besides the relation-aware attention over entity types, we also apply another two aggregation strategies, i.e., average pooling and max pooling.

As shown in Figure \ref{aggregation_pr}, \textbf{with avg-pooling} and \textbf{with max-pooling} denote the relation-aware attention is replaced with the average pooling operation and the max pooling operation, respectively. It is observed that the proposed relation-aware attention over the entity types is more robust in the PR curves which indicates that this attention mechanism is capable of selecting the important information from entity types for different relation classes.

Note that we also report the PR curve of RESIDE \cite{vashishth2018reside} in Figure \ref{aggregation_pr}. The performance of DNNRE all outperforms RESIDE by a large margin with any chosen aggregation strategies, which demonstrates that the dynamic design of our model using the entity type information is more effective.

\begin{figure}[htb]
\begin{center}
\includegraphics[width=.8\textwidth]{coarse_pr.eps}
\caption{\label{coarse_pr} Performance comparison of the  DNNRE with different granularities of entity types.
}
\end{center}
\end{figure}

\subsection{Effects of Different Granularities of Entity Type} \label{diff_grained}

In this subsection, we investigate how the different granularities of the entity types affect the discriminative capability of the generated dynamic parameters. The results are reported in Figure \ref{coarse_pr}, in which the \textbf{coarse-grained} denotes that the dynamic parameters of the attention and classifier are generated by the coarse-grained entity types, i.e., the 38 coarse types which form the first hierarchy of FIGER types.

On the one hand, DNNRE with fine-grained entity types outperforms the DNNRE with \textbf{coarse-grained} entity types by a large margin. That means the fine-grained entity type is helpful for the performance improvement of DSRE, which is also validated by \cite{liu2014exploring}.

On the other hand, considering RESIDE \cite{vashishth2018reside} also utilizes the 38 coarse types of FIGER \cite{ling2012fine}, we compare the DNNRE (\textbf{coarse-grained}) with RESIDE. Note that RESIDE utilizes lots of external information, including the entity type, relation alias and syntactic information of sentence, which is encoded by a Graph Convolutional Network \cite{kipf2017semi}. However, only using the 38 coarse types, the performance of our DNNRE is still comparable to RESIDE, which indicates that the way of generating dynamic parameters by the entity types is more effective than utilizing them as input constraints \cite{liu2014exploring,vashishth2018reside}.

\begin{figure}[htb]
\begin{center}
\includegraphics[width=1.\textwidth]{casestudy.eps}
\caption{\label{case} Examples to evaluate DNNRE for the style shift problem. On the left side, the entity pairs, entity types, and relation classes of two bags are shown. On the right side, the estimated probabilities (confidence scores) for detecting the ground-truth relation are shown.
}
\end{center}
\end{figure}

\subsection{Case Study} \label{case_study}

Figure \ref{case} uses two examples to show how DNNRE addresses the style shift problem. Two models are used to conduct the case study, i.e., DNNRE and its variant (\textbf{W/O Type}), whose parameters are not incorporating the entity type information.

The first example expresses the relation by the keyword ``\textit{chief marketing officer}''. However, in the second example, the tail entity type changes to ``\textit{educational institution}'', and the sentence expresses the same relation by a different keyword, i.e., ``\textit{economist}''.
In both examples, the proposed DNNRE is able to adjust the model parameters according to entity type information and produce higher prediction scores. The confidence scores from DNNRE are 0.95 and 0.96 for these two examples, respectively. On the contrary, the confidence scores from DNNRE \textbf{W/O type} only obtain 0.71 and 0.59, respectively. The results demonstrate that DNNRE can utilize the entity type information to compensate for the relation representations with more varieties for handling the style shift problem.

\section{Conclusion}

In this work, we propose a novel Dynamic Neural Network for Relation Extraction (DNNRE), whose parameters are determined by the query entity types and relation classes. The dynamic design of our model benefits for the potential style shift caused by keyword variation under different entity types. Besides, the entity type information can also be trained cross different relation classes and further benefits for the long-tail relation classes.
Through extensive experiments, we demonstrate that the proposed method is effective for improving the DSRE accuracy.

In future work, we will focus on the following directions: (1) We will explore how to better model the style shift problem with different information inputs, e.g., the entity description information. (2) The connections between different relation classes can be further utilized to boost the performance of DSRE. (3) Entity typing can be incorporated into our DNNRE via a multi-task training manner and further benefits for the improvement of the DSRE performance.

\section*{Acknowledgment}
This work was supported by the Key Research and Development Program of Sichuan Province (2019YFG0409).

\bibliographystyle{elsarticle-num}

redpajama_set_name: RedPajamaArXiv


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text:
27 November 2018 / interview
How will bots change the world?
We have started an interview series, where we bring you the stories of growth hackers, entrepreneurs, marketers and influencers in the hope that you can learn a few insider tips on how they tackled the barriers to achieve success.
To kick off the first episode of our interview series, we had influencer, founder, and bot podcaster Chad Oda join us! Chad has been in to world of Bots for a bit now and has started the Botrepreneurs Facebook Community, the Bot Podcast, and is currently the Head of Consulting at Chat Mode. He also helps run the The Chatbot & Voice Meetup in Seattle, Washington.
If you are into listening rather than reading, please subscribe and tune into one of the following
Breaker.audio
or listen now on Anchor
🤖 What got you into Bots?
It's always interesting, "origin stories", especially with new technologies. I started back in 2016 with bots, when I was running an automation-workflow consulting firm and it's sorta funny that my co-founder had read something that day about Chatbots, and it clicked as like "whoa, you can automate messaging channels and scale interactions out". It was even funnier that just a few weeks later, we had an opportunity with a large client that wanted to get some automation done and from there we realized that chatbots can be used as the solution.
😎 What drew you to become a Bot Evangelist?
I didn't really plan on being an influencer, but when I was getting into bots, I didn't feel like I was an expert, but, I was ready to talk to the smartest people in this space. This is where the idea of the podcast came in for potentially benefiting the community, but also helping learn about it myself. My whole objective and mission is to really try to surface what's tangible and feasible in the bot space and cutting through the noise.
🌍 How will bots change the world?
Bots have moved from command-line interfaces, to web-based interfaces, and then to mobile interfaces, making them powerful tools for startups and enterprises alike. The potential that exists is the friction and intuitiveness for interacting with a conversational interface. This gives someone who is 70 years-old a chance to experience an interface in the same way someone at 4 years old can. What we get are interfaces that render more visual interfaces useless at times in favor of something more accessible.
👀 Tell us more about the Bot Summit in Berlin you attended?
The Bot Summit really proved one thing at the moment: Bots are coming back from the Trough of Disillusionment. The sessions showed a renewed sense of confidence that bots can be both useful in the world of enterprise and in the consumer world, and that renewed sense was a major forefront of the event.
🌐 What do you think is the future of bots in Enterprise/B2B?
In the near term, the enterprise has a lot of the major opportunities to hit their true potential, as mentioned with the consumer/marketing side, but also with customer support, and even in the concept of internal chatbot tools and automating workflows internally. There's a lot of potential in how bots can not only help teams, but also the individual. Something around managing a business trip, or keeping a personal workflow, or even something like working with expenses and having a conversation with a bot to get this done.
👜 How about future of bots in Consumer Industry?
I believe that, if we look at the trends, messaging is the main channel for consumers, with messaging platforms having more active users than the top social apps themselves. This indicates a change in ways consumers want to interact, much like they do with friends, family, co-workers, etc. And from that, they want to feel the same things from brands and companies. We can see opportunities to make an impact in industries like eCommerce or hospitality in the situations where on-demand help can be really impactful in the long-run. We can imagine a world where we're just interacting with a voice interface and you'll be able to go down the sales process in a major brand and, perhaps, this will be done with one centralized virtual assistant rather than multiple different interfaces you'll have to switch around. We're also going to see enterprises tap into their data to allow conversational interfaces to become more personalized and a first-point of contact for consumers who will need help.Going even further, location-based chatbots could be in the near future as well, with the concept of creating bots that will activate when you walk into a department store and gather your data to really give you the proper recommendations, maybe even based on searching you've done on a website.
👥 You've built a pretty successful community around the niche of bots, what's been your biggest lesson in starting an online niche tech community? What made you actually start up a full community?
It's all in line with the idea I had for the podcast: It's the idea of "how can we bring more people together?" and "how can we figure things out together?". To me, it's about hearing from smart people is a way to learn fast, and utilizing that opportunity and focus on video content to really build out an engaged audience. For our offline community, our meetup, it was the same approach of trying to bring passionate, smart people together, just in an offline space. I think the more people we're able to bring into a community and learn from one another, the faster we can solve hard problems and make things happen through pooling resources and knowledge, and understanding that is probably one of the biggest lessons in starting a community.
📢 Tell us a little bit about pitching bots to businesses?
I think first of all, there is a component here that actually involves companies that are creating the bots or offering the tools for creating bots. In any new technology, there's always an idea of trying to hit every use case, and people forget the business case. There were definitely companies in the early days of bots that hyped things a little too far, and not really talking about the business case. It's not atypical, and it goes back to simply thinking about how we market business solutions in general.It always comes down to what a business needs and understanding the benefits of taking a chance on new technology, like bots in this case. Specifically, get the innovation departments or RnD departments of companies on board is really the key to properly selling bots. Also really talking to the customer support needs of a company, as there has been a lot of talk around that as well. At the end of the day, it's exciting for people interesting for people who live and breathe technology like us, but to the major decision makers and stakeholders, it really comes down to the ROI and results.
🔄 What do you think it'll take for bots to come out of a slump in the hype cycle?
I certainly believe that bots are coming up the initial slope out of the trough of disillusionment right now, and the reason I believe that is that a lot of the noise of "AI will solve everything" and the crazy expectations for chatbots are finally coming to a close while more realistic expectations and use cases are starting to rise back up. While there have been some companies that have been able to keep up from the days of hype, today, we see more select companies that have started to really "figure it out" on how to understand best practices and processes that will better appeal to certain use cases and lead to success in the long-term.
💬 What are your thoughts on audio interfaces (Alexa, Homepod, etc) and the future of voice bots?
I think from a tech perspective, for the most part, the tech is similar, but that being said, with voice user interfaces, channels don't exist. For Google Home, you could say there is some level of channel through some Android phones, but for Alexa, there's constantly new devices at different prices coming out to try to build out that channel.That said, chatbots are more mature than audio interfaces, and it really comes down to discoverability. Until that's really solved, voice will still be an adjunct channel, and expanding discoverability to the skills themselves is key. Even really understanding what people want from voice interfaces is still something that needs to be focused in on. Then it can go into opportunities to monetize, which is where the bottleneck starts, where people may not even know what they want out of their voice-activated device, and therefore, unsure of what they would even be purchasing. I think within the next couple years though, we'll see some more complex user interfaces, but it will still take some more time to really get it right in the world of voice interfaces.
😎 What do you think of Non-AI conversational interfaces?
I come back to the context of "it depends" on the matching the "flavor" of chatbot with the proper use case, whether it's a rule-based chatbot, NLP chatbot, or even a hybrid chatbot. I think people don't give enough credit to rule-based, non-AI chatbots that can still drive quite a bit of revenue for brands once you dig in.With technology right now, NLP is pretty good but I think there's still a lot of work to be done. When we think about it, bots really can't quite do non-sequitur or good context management right now like humans can. Some of the most basic/common things that happen in our conversations with friends, family, and co-workers, most bots can't quite handle that both in functionality and scale. And right now, I think there's a lot of different ways and methods it can go in the overall. And to conclude it, we just look at the potential of a lot of these different options and flavors, but at the end of the day, it'll all come back down to use cases and results when we think of where chatbots could go.
Here is the Full Interview
Nikhil Vimal
Starting the largest Bot Directory on the Web
Seth is one of the few who had sensed the wave of bots that were to hit the market and had a good idea on how it would disrupt the market. Let's hear from him on how he started BotList and what's his future plans.
The Secret to a High Converting Landing Page that You Should Know
Making an awesome landing page about your product or service is half the work. Having a sales funnel in place to convert your leads is more important. See how you can do this using chatbots.