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Local farms in Easthampton, Sunderland, to expand and upgrade thanks to state grants
June 10, 2013 Posted in: Local Heroes in the News
Daily Hampshire Gazette
Rebecca Everett
In the more than 30 years that William and Susan Chicoine have been raising grass fed beef cows at Chicoine Farm on Oliver Street, a lot has changed in area agriculture.
One of the biggest developments is the increased interest in buying local food, William Chicoine said. In addition to snapping up Hadley asparagus and Valley sweet corn at the now abundant farmers markets, customers are also seeking out grass-fed beef and other local meat.
"People want to eat healthy and they've discovered the benefits of grass-fed beef compared to commercial beef," he said. "People are just becoming more educated about it, and that's created more interest and more sales."
The demand is there for the farm's beef, he said, but it isn't as accessible to customers as he'd like. Other than selling to local restaurants and at farmers markets, they sell directly to customers at the farm by appointment. But Chicoine said there isn't a real farm stand; the meat freezers are located in the farm's crowded workshop building, so it's not a very customer-friendly spot.
Soon, thanks to a $50,000 grant from the state's Department of Agricultural Resources, Chicoine will be able to add onto the building to create a farm store.
Chicoine Farm was one of nine farms in the state that received a total of $700,000 in Agricultural Preservation Restriction Improvement Program grants. The grants are given to farms with land that is protected from development through the state's APR program. In the Pioneer Valley, Chicoine Farm and three others received a total of $200,000 in grant money.
Also thanks to a grant, Warner Farm in Sunderland is expanding its facilities to meet the demands for produce for CSA shares, as well as to better serve those visiting Mike's Maze, one of the area's first corn mazes. The farm received a $75,000 grant for an addition to the Corn Cafe at the maze, where patrons can buy food and soon, farm products. It will also cover the costs of retrofitting a garage into a more modern produce packing facility with more cold storage space.
"We're trying to upgrade facilities around the yard here. Especially with the amount of traffic we get because of the maze, it's important to have a nice, clean-looking operation," said David Wisseman, Warner Farm's field manager and son of owner Michael Wisseman. "And these were upgrades we really needed to make, so the grants are a big help."
Thomas Farm in Sunderland also received $25,000 to build an addition to a dairy goat barn that will allow the farm to increase its herd and put in goat cheese processing facilities. A $50,000 grant will help a new farm, Ridale Genetics, get off the ground on a parcel of land off of Lyman Flat Road in Cummington. The funds will go toward building a barn to hold hay and equipment.
"These grants will help support our local farms in their efforts to ensure a sustainable future for their farms," said Massachusetts Energy and Environmental Affairs Secretary Rick Sullivan in a press release. "I am pleased to award these projects that will not only benefit the agricultural community, but the Commonwealth as a whole."
Chicoine Farm
The Chicoine family has farmed the land around 116 Oliver St. since 1933 when Chicoine's father, Henry, started a dairy farm. William Chicoine started building a herd of grass fed beef cows there in the 1970s, before terms like "grass fed" and "antibiotic free" were well known in the local meat market.
Now, he grazes between 60 and 70 cows on about 120 acres, mostly on Oliver Street. The cows, of various beef breeds, are fed only grass and are not treated with antibiotics, growth hormones or steroids, he said.
Chicoine said that as soon as the state grant money comes through, he will begin work on a 672-square-foot addition that will be the new farm store, as well as another 768-square-foot barn addition that is also supported by the grant. That addition will create more hay and equipment storage space.
The farm store, once it is built, will offer various cuts of beef during set business hours or possibly one day a week, Chicoine said.
He hopes that the more attractive farm store will draw people to the farm to buy meat. Selling meat at the farm is much easier than offering it at farmers markets, which require them to load the frozen meat in a truck and, while at the markets, to keep it cold no matter how hot it is.
"Getting the customers to come to you is the best scenario," he said. "And they can come see the cows, the pigs, the chickens."
Since the 1720s, 10 generations of Warners have farmed the land in Sunderland, including fields on Old Amherst Road, the home of pick-your-own strawberries, and South Main Street, where Mike's Maze is located.
The Warners of the 18th century probably would have been surprised to see the South Main Street corn field cut into the shape of the likes of Mona Lisa, Babe Ruth and the iconic Campbell's tomato soup can. But the maze is an agritourism attraction that serves as an additional source of income for the farm, which can be especially important in years where too much or too little rain affects crop yields.
The thousands of visitors to the maze can grab a burger, hot dog and other refreshments from the Corn Cafe now, but with help from the APR Improvement Program grant, the farm will be able to offer more products there soon. Wisseman said the addition will double the size of the cafe.
"The hope is it will give us more space and allow us to add value-added products, like jams, or our frozen strawberries," he said.
The second project they will undertake is to renovate a garage there into a produce storage and packing facility. Right now, Wisseman said produce is cleaned and packed in an old barn with a small cooler.
"We'll be moving into the garage and it will be much nicer on the inside, with a new cooler, all stainless steel, and a new loading dock and other retrofits," he said. The facility will comply with new government recommendations for packing facilities, he said.
One of the best features of the new facility will be the cooler, which will be twice the size of the current one and was made possible by a 2012 Agricultural Energy Grant from the state. Wisseman said the CSA the farm offers to residents in the Boston area requires them to clean and package a large amount of produce at one time.
"The night before we would head out to the CSA drop, the cooler would be maxed out with all the shares in it," he said. "So the CSA is one of the big reasons this grant was so helpful," he said.
Wisseman estimated that the $75,000 grant will cover almost all, if not all, of the costs for the two projects.
Tags: Chicoine Family Farm, Warner Farm
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Wall Street ends down; Broadcom warning hits chip stocks
By Caroline Valetkevitch
NEW YORK (Reuters) - U.S. stocks ended lower on Friday as investors were cautious going into next week's Federal Reserve meeting, while a warning from Broadcom of a broad weakening in global demand weighed on chipmakers and added to U.S.-China trade worries.
Shares of Broadcom Inc fell 5.6% after it cut its full-year revenue forecast by $2 billion, blaming the U.S.-China trade conflict and export curbs on Huawei Technologies Co Ltd.
Other chip companies, which both source product and sell heavily in China, dropped sharply. The Philadelphia Semiconductor index tumbled 2.6%.
Investors are bracing for next week's Fed meeting in light of recent market expectations that the U.S. central bank could cut interest rates as much as three times this year.
Some strategists say stocks are primed for a selloff should the Fed fail to take an even more dovish tilt.
The S&P 500 index has so far gained 4.9% in June and registered a second straight week of gains on Friday, largely on the rate cut hopes.
"We're going to be on pins and needles until we get some indication from the Fed," said Brian Battle, director of trading at Performance Trust Capital Partners in Chicago.
"That's what matters. Everybody is betting that the Fed is going to cut rates, probably not in June but soon. That is a very crowded trade."
The ongoing trade battle between the United States and China also gives investors reason to play it safe ahead of the weekend.
"This is kind of a wait-and-see mode. People are staying very close to their benchmarks," said Robert Phipps, a director at Per Stirling Capital Management in Austin, Texas.
The Group of 20 summit at the end of the month may yield progress on a deal.
The Dow Jones Industrial Average fell 17.16 points, or 0.07%, to 26,089.61, the S&P 500 lost 4.66 points, or 0.16%, to 2,886.98 and the Nasdaq Composite dropped 40.47 points, or 0.52%, to 7,796.66.
All three major indexes posted gains for the week: the Dow rose 0.4%, the S&P 500 gained 0.5% and the Nasdaq added 0.7%.
During the session, shares of Apple Inc slipped 0.7%, with Broadcom a major supplier to the iPhone maker.
Adding to global demand worries, Chinese data pointed to the worst slowdown in industrial growth in 17 years. China's industrial output growth in May slowed below expectations and showed signs of weakening demand.
In a bright spot, data showed U.S. retail sales increased in May and sales for the prior month were revised higher, suggesting a pick-up in consumer spending that could ease fears the economy was slowing down sharply in the second quarter.
Online pet products retailer Chewy Inc ended up 59% in its market debut, joining a host of high-profile names, such as Lyft Inc and Uber Technologies Inc, that listed on U.S. stock exchanges this year.
Declining issues outnumbered advancing ones on the NYSE by a 1.53-to-1 ratio; on Nasdaq, a 1.79-to-1 ratio favoured decliners.
The S&P 500 posted 37 new 52-week highs and 3 new lows; the Nasdaq Composite recorded 47 new highs and 82 new lows.
Volume on U.S. exchanges was 5.85 billion shares, compared to the 6.83 billion average for the full session over the last 20 trading days.
(Additional reporting by Sinead Carew; Editing by Arun Koyyur, James Dalgleish and Sonya Hepinstall)
1Wall Street ends down; Broadcom warning hits chip stocks

redpajama_set_name: RedPajamaCommonCrawl


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A protester asks Jon Slater, IHA senior medical director, how 24/7 emergency department care will be brought back to Princeton.
Princeton protesters hold second rally to demand 24/7 ER care [VIDEO]
Protesters held a second rally in a week as Interior Health representatives met privately with Princeton town council.
Michaela Garstin
May. 29, 2012 8:00 p.m.
Protesters held a second rally in a week as Interior Health Authority representatives met privately with Princeton town council on May 28.
A crowd asked IHA employees how they were planning to bring back 24/7 emergency department care to Princeton.
"We're here to send Interior Health… a message that if they're not going to come to our community with a solution, we're done talking with them," said Save Our Hospital Coalition president Spencer Coyne at the rally.
Save Our Similkameen president Solana Allison said she didn't expect the meeting to produce a solution.
"We have heavy industry that runs 24/7 shifts, but we have no 24/7 ER. Employers are concerned with issues with liability and concerned with workers' safety," she said.
Two external agencies will be used to help recruit locums, said Acting Mayor Jason Earle.
"IHA and town council are going to be working together to find a stable solution for years to come," he said.
The trick to providing complete ER coverage is to attract locums and eventually a new doctor, said IHA senior medical director Jon Slater after the meeting.
"The long-term (solution) is to bring physicians to town to establish practice… In the short-term we need to get people from out of town to help us out," he said.
Protesters held a rally on May 22 when Interior Health representatives met with local business managers and politicians.
Princeton Hospital emergency department is closed from midnight to 8 a.m. Monday to Thursday.
Video by Lisa Carleton and Michaela Garstin
Pinnacle Renewable Energy holds open house for proposed Prince Rupert terminal
Enbridge spends millions to promote pipeline


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Two Emmet county boys today were reported killed in action. Pfc. Donn R. Mauck of Estherville was killed in Germany Jan. 27 and Second Lieut. E.K. Vandeveer of Dolliver was reported killed Jan. 23 in Belgium.
Private Mauck, 20, of Estherville, is the son of Mr. and Mrs. C.P. Mauck of Estherville who were informed of his death this morning when they received a telegram from the United States war department. Private Mauck was serving in the infantry.
Private Mauck, who was born Oct. 6, 1924, in Estherville, was active in school basketball and dramatics. He was a member of the Future Farmers of America and was graduated from Estherville high school May 28, 1942.
Private Mauck was inducted into the armed forces March 6, 1943 at Camp Dodge in Des Moines at the same time that Pfc. Earl Holroyd of Estherville was inducted. Private Holroyd was reported killed in action Jan. 11 in Belgium.
Private Mauck took his training at Camp Roberts, Calif., New York City, Camp McCain, Miss.
Private Mauck is survived by his parents, an older brother, Corp. Max, also in the service, and enroute overseas, and a younger brother, Billy, who will graduate from the Estherville high school this spring. Corporal Mauck is a gunner in the air force.


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HomeRiflesSemiauto RiflesYeah, the AR15 is Now Becoming C&R Eligible
Yeah, the AR15 is Now Becoming C&R Eligible
February 7, 2017 Ian McCollum Semiauto Rifles, Video 13
Given the status of the AR15 as the preeminent military rifle still today, it comes as a surprise to many people to learn just how old the gun really is. Civilian production of the SP-1 model by Colt actually began in 1964, which means that early SP-1 rifles started becoming Curio & Relic qualified back in 2014. Right now in 2017, more than 10,000 C&R AR15s exist – and this is one of them.
Ishapore SMLE MkI** India Pattern (Video)
Gahendra: the Nepalese Martini (or not)
Winchester Lever Action Development: Model 1876
June 11, 2017 Ian McCollum Lever Action Rifles, Video 20
Rifle NWMP Carbine While the Model 1873 was a very popular rifle, its pistol caliber cartridge did leave a segment of the market unaddressed. Winchester wanted a rifle that could chamber the larger and more […]
Vickers-Berthier 1919 US Trials Rifle (Second Type)
November 6, 2018 Ian McCollum Prototype, Semiauto Rifles, Video 13
This rifle sold for $31,625 at Rock Island on December 1, 2018. After designing the bolt action rifle that bears his name, Andre Berthier went on to experiment with self-loading designs. He developed a light […]
America's First Contract Pistol: North & Cheney Model 1799
September 3, 2019 Ian McCollum Flintlock, Single-shot Pistol, Video 7
Lot 1421 in the September 2019 RIA auction. In 1798, the US Congress allocated a huge sum of money – $800,000 – to the procurement of military equipment to supplement the output of the Springfield […]
Mike Baryla says:
Doesn't C&R status only apply if the rifle is in its original configuration?
Zorg says:
No, it doesn't work with full automatic weapons
eon says:
True, but the Colt SP-1 was a semi-automatic only civilian version of the Armalite. As such, it falls under the C&R category once it passes 50 years of age.
The Armalite AR-180s will begin to reach that status this year, as the first production run was in late 1967. The Ruger Mini-14 early models will qualify in 2023.
Most of the Universal, Plainfield, and Iver Johnson M-1 Carbine clones will be C&R eligible within the next five years as production of them ran from the early 1960s to the mid-1970s for the most part.
I believe the commercial semi-auto only Beretta BM-59 series already qualifies, as production of those lasted only from 1961 to 1965. Early civilian CETME rifles (Spanish version of the H&K 91 series) also should already qualify, as they were produced from late 1959 to mid-1967.
Cherndog says:
Sadly for us intellectuals who happen to have interests in the firearms of half a century past, naïve gun-control advocates seem to believe that just having any gun will allow a person to turn into some super crazy serial killer who will litter the floor with bloodied police badges and severed heads. And thus, those idiots will think that EVERY member of the AR family was likely made "EXTREMELY RECENTLY" if only because the stock is plastic and refuse to believe that any AR-15 (no matter how old) could possibly qualify as a curio/relic piece.
Did I mess up?
Daweo says:
"stock is plastic"
Which, BTW, was introduced first in sporting fire-arms, namely Tenite http://extranosalley.com/tenite-gun-stocks/ was used in 1940s, for example in Stevens 22-410, it was non-automatic non-magazine fire-arm featuring two barrels (one for .22 rim-fire other for .410 shot-shell as name implies) and later used in Remington Nylon 66 – .22 rim-fire self-loading rifle.
is: "(…)later used(…)"
should be: "(…)later Zytel-101 was used(…)"
Rick Randall says:
Actually, C&R status applies to NFA weapons (including machineguns).
Of course, all that means is that you can buy one across state lines and only pay one registratuion tax (and wait period), because you don't have to filter the sale through an NFA dealer in your home state. When transferring a non-C&R NFA "firearm" across state lines,the seller has to transfer the gun to an NFA dealer in the buyer's state, including the registration tax (unless the seller is also an NFA dealer – dealer to dealer transfers are tax exempt) and waiting for the transfer TO the receiving dealer to be approved, and ONLY THEN does the actual buyer apply to have the gun transferred from the dealer to himself.
You still have to go through the same NFA registration process when it is a C&R eligible gun — only you can skip the transfer dealer (and thus the time and tax are halved – only one transfer approval wait and one $200 tax, instead of two each.)
Hoodoo says:
Well, becoming eligible and treated as eligible are two different things. I just bought a 93 Mauser made in 1896 (clearly marked on the receiver) that a seller would only send to an FFL and the dealer made me fill out a 4473. "Oh look a squirrel"…what I write now when I get off subject on a thread. I want to restore a Spanish Crest onto this 93 as I am "restoring it" to Spanish American War configuration so it will inspire me to kick a friends butt who has piece of crap Krag in our own Ian inspired little Spanish American War challenge (not going to film it, don't have the talent or equipment for that). Know anyone who does this sort of work, maybe with etching (if I can't find anyone I will do the etching myself methinks-can't hurt it). I need a rubbing of the crest though to get the detail and the scale correct (Can anyone help me out on that?). Photos so far are not sufficiently detailed. I am not trying to make a perfect crest but something I won't be embarrassed about either.
McThag says:
If I recall correctly the screw front pin is two-fold. First it makes it (a little) harder to put an M16 upper on an SP1 lower; second the no-fence lower's pin was non-captive and needs an counterbore operation on the front lug.
The rifle shown is such a mix-master!
This gun was and still is very advanced for its age; given particular design intent (such as small bore-high velocity projectile introduction). It was not particularly light when compared with other rifles, but it was lighter than previous model in service and that was selling point. Its conception was setup with preference of use of forgings (which made it labour intensive) but hey, this was America.
I admit that I was, technically speaking, very excited when I first read about this rifle while still over in Europe and even continued to praise it while in military service. Later I realized how potentially dangerous it was – praising enemy's weapon. Ironically, bit later on I had the chance to actually work with it. One's future is unpredictable…. isn't it.
How original must a firearms be for C&R status? What degree of originality is required? BATFE's FAQ's on the subject suggests that the firearm needs to be in original condition. Does the date the receiver left the factory determine that a firearms is a C&R? For example, I do not believe the rifle shown in the video is in original condition. This would be less of an issue let's say with a restored older firearm than one made of interchangeable parts like the AR15.
The gun in the video appears to me to consist of an upper receiver assembly from a gun made in the late 1980's and a lower made from mixed parts. The upper receiver has a transitional tear drop forward assist that did not appear in production until the mid to late 1980's. The chromed bolt carrier is also wrong. Colt produced no chromed bolt carriers (with the exception of a run of 1,000 Colt Guard finished guns in the 203,xxx s/n range years later). A correct 1967 SP1 would have a slick side parkerized bolt carrier and a parkerized bolt (the chromed bolt was phased out in the preceeding year.) A correct 1967 SP1 should have a pronged flash hider secured with the unique textured crush washer with a diagonal cut. The A1 birdcage was not phased into Colt's civilian AR15 production until at least two if not three years later. Then the dust cover has the large square pad that was phased out early on in 1966. The lower receiver is also questionable as a C&R. The butt stock is incorrect appearing to be an A2 butt stock in the modern configuration with trap door, fixed swivel and 13.5″. The correct butt stock has a rounded butt with no storage compartment, hinged swivels and a 13″ LOP. Interestingly, the trigger and hammer pins appear to pre-date 1967 and have the dimpled manufacturing artifact associated with the earliest SP1's of 1964-1965 serial ranges.
So this video is an interesting illustration of the potential dangers of collecting curios and relics in more modern firearms. "ATF has recognized only complete, assembled firearms as curios or relics. ATF's classification of surplus military firearms as curios or relics has extended only to those firearms in their original military configuration. Frames or receivers of curios or relics are not generally recognized as curios or relics." https://www.atf.gov/firearms/qa/what-firearms-are-considered-be-curio-and-relic-firearms
Manufacture date of 50 years ago or more is the critical date. And the *receiver* is the magic part, because "the receiver is the firearm". That does not mean you can take a C&R receiver and do whatever you want with it… although you *can* restore a stripped receiver. (And, IIRC, a stripped receiver by itself is not considered C&R eligible, because it wasn't sold that way 50 years ago.)
Post-manufacture parts do *not* alter that date, *unless* they change the gun to a configuration that doesn't correspond with that date. This allows you to "restore" a gun that has been "Bubba'd" into a non-C&R eligble configuration, using later parts that match the C&R configuration.
You *might* be able to build a faux-carbine/aircrew survival rifle (which they had 50 years ago) off a C&R AR15 receiver, but you couldn't, for instance, drop a flat top upper and a six position carbine buffer on it and maintain it as a C&R gun.
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Home | Press Centre | Press Releases | AKU/Nova Symposium tackles ethics in stem cell research
Panelists, from L to R: Ilhan Ilkiliç, Istanbul University; Azim Surani, University of Cambridge; Jorge Soares, Portuguese National Council of Ethics for the life Science; Arnold Kriegstein, University of California; and Helena Pereira de Melo, NOVA University
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The NOVA University Rector João Sàágua and the Aga Khan University President Firoz Rasul sign a memorandum of understanding to facilitate collaboration in a variety of areas, including health sciences, humanities and social sciences, and media and communications.
From L to R: NOVA University Rector João Sàágua, Prince Aly Muhammad Aga Khan, Aga Khan University President Firoz Rasul.
AKU/Nova Symposium tackles ethics in stem cell research
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Experts discuss emerging concerns at a symposium co-hosted by Nova and AKU in Lisbon
Lisbon, Portugal, 13 June 2019 - New discoveries in stem cell research have raised the hope among scientists that there will be breakthrough cures for common diseases ranging from heart disease to diabetes, but new ethical and regulatory questions have surfaced, according to experts at a symposium co-hosted by Nova University and the Aga Khan University (AKU).
Stem cells can be used to halt or even reverse chronic diseases by repairing or replacing tissues or organs. A good example is bone marrow transplantation, which is used for treating certain blood and immune system disorders. Some bone, skin and eye injuries and diseases can be treated by grafting or implanting tissues.
"Since these cells offer a tremendous hope for alleviating human suffering, researchers, industry and multicultural societies need to be on the same page with agreed-upon regulatory policy and guidelines that ensure ethical activities, transparency and best practice," said Professor Arnold Kriegstein, founding director of the Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research at the University of California, San Francisco (UCSF).
Although there has been an amazing progress in stem cell science during the last 10 years, most of the advances lead to ethical challenges, said Dr Azim Surani, director of Germline and Epigenomics Research. "For example, if a heritable monogenic disease can be treated, should we use gene editing or not? These are the questions that society needs to address."
The new ethical questions relate to extending the culture of surplus human embryos generated for in-vitro fertilisation or test-tube babies, generating gametes (reproductive cells) and artificial embryos from stem cells, making animal-human chimeras, and genetically editing the human embryo.
There is also the rising tide of victims - unsuspecting subjects of risky experimentation or unproven or fraudulent treatments. Professor Timothy Caulfield, research director at the Health Law Institute, University of Alberta, calls this "scienceploitation": "Now you see stem cell, genetic, and increasingly, microbiome research being exploited to sell a host of ridiculous products," he said.
While "scienceploitation" has been taking place for years in wealthy countries - and is increasing - the same is now true in the developing world. Ironically, countries with high poverty rates stand to benefit the most from ethically responsible progress in the field, said Professor El-Nasir Lalani, founding director of AKU's Centre for Regenerative Medicine and Stem Cell Research. "It would be unethical if equal access to new therapies were not possible in developing countries at the outset."
"Humanity must come to grips with the host of new discoveries and immense opportunity this presents," said Professor Lalani. "Now we as a global community are tasked with making sure this doesn't become a runaway train leaving behind the ethical considerations. To stop the train completely would also be unethical, as the hope for breakthrough cures from stem cell research is greater than ever."
Professor Lalani also spoke about building a research capacity partnership between AKU and UCSF in the development of a comprehensive and integrated research programme in stem cell biology and regenerative medicine. "Being a research-led University," he said. "we believe that investing in stem cell research is a step forward toward achieving the United Nations' Sustainable Development Goals."
More on the event website.
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On the occasion, the Nova University Rector João Sàágua and the Aga Khan University President Firoz Rasul signed a Memorandum of Understanding to facilitate collaboration in a variety of areas, including health sciences, humanities and social sciences, and media and communications. They expressed their commitment to foster and strengthen the partnership in order to improve the quality of life of vulnerable populations, to advance knowledge through cutting-edge research, and to promote international and cross-cultural cooperation and understanding.
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This is a rear fully adjustable sight, white outline, for the Sig Sauer models P220, P225, P226, P228. This sight uses the original Sig Sauer stock front sight and sits low profile when mounted. Additional wrenches are included in the package. These are top quality LPA brand sights made in Italy. The rear sight is fully adjustable, for elevation and windage, so you may adjust it in any way for your liking to correct your pistols point of impact. The sight set is made of steel construction, not plastic. These sights use the original dovetails/slots on the pistol slide. These are the best sights on the market today to improve your pistols sighting. Will not work on Novak dovetails. Wil not work on Sig Mosquito.


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\section{Introduction}
Machine translation (MT) is a  sub-field of natural language processing (NLP) that investigates how to use computer software to automatically translate text or speech from one language to another without human involvement.
MT is one of the prominent tasks in NLP that is tackled in several ways \cite{bojar2017findings}. The first MT research began at about 1950s and in 1952 the first International Conference on Machine Translation was organized at the Massachusetts Institute of Technology (MIT). It has long research history and experienced four stages, namely, Rule-based MT \cite{forcada2011apertium},  Statistical MT (SMT) \cite{koehn2007moses}, hybrid MT, and Neural MT (NMT) \cite{cho2014properties,kalchbrenner2013recurrent}.

The most severe drawback of the rule-based method is that it
has ignored the need for context information in the translation
process. It is highly dependent on hand-crafted features. Phrase-based SMT (PBSMT), the most prevalent version of SMT, generates translation by segmenting the source sentence into several phrases and performing phrase translation and replacement. It may ignore the long sentence dependency and require high computing devices \cite{baniata2021transformer}. Recently, using deep neural networks for MT task has received great attention. NMT also improves training procedures due to the end-to-end procedure without tedious feature engineering and complex setups. NMT employs such techniques as recurrent neural network (RNN) \cite{bahdanau2014neural}, convolutional neural network (CNN) \cite{gehring2016convolutional}, and self-attention network (Transformer) \cite{vaswani2017attention}.

Transformer models with the pre-training approach is a new NMT strategy entirely based on attention mechanisms proposed in 2017 \cite{vaswani2017attention}. Among the different neural network architectures, the Transformer model has emerged as the dominant NMT paradigm \cite{raganato2018analysis,yigezu2021multilingual, tonja2021parallel}. It has become the state-of-the-art model for many artificial intelligence tasks, including machine translation. In terms of model, the Transformer-based pre-trained models are fast to fine-tune, highly accurate and has been proven to outperform widely used recurrent networks \cite{zhang2018improving,baniata2021transformer,yang2020survey}.

The focus of MT research for the Amharic language has been on rule-based and SMT methods. In this work, we used the transformer model as a baseline translation system to explore the applicability of Facebook M2M100 multi-lingual pre-trained language model for Amharic-English translation in both directions. Furthermore, this research work investigated the impact of normalization of the Amharic homophones on Amharic-English MT tasks.

The main contributions of this work are:
\begin{enumerate}
    \item Exploration of the Amharic-English and English-Amharic machine translation tasks.
    \item Introduction of the first large-scale publicly available Amharic-English translation parallel dataset.
    \item Development and implementation of state-of-the-art Amharic-English translation models.
    \item Investigation of the effect of Amharic homophone character normalization on the machine translation task.
\end{enumerate}

The rest of this paper is organized as follows. Section \ref{amharic_langauge} presents a detail description of Amharic language while Section \ref{motivation} shows the motivation for this research. In Section \ref{related_work}, we review related work. Section \ref{dataset} describes the existing parallel corpus and the collection of a new corpus from the news domain. The general pre-processing steps applied to both corpora are presented in Section \ref{data_pre}. Section \ref{models} discusses the proposed NMT models and Section \ref{experiment} gives the experimental results. In the end, Section \ref{conclusion} concludes the paper and sheds some light on possible future work.

\section{Amharic language}\label{amharic_langauge}
 Amharic is the second most spoken Semitic language next to Arabic which has its own alphabet and writing scripts called 'Fidel', that was borrowed from Ge'ez, another Ethiopian Semitic language.  Fidel is a syllable-based writing system where the consonants and vowels co-exist within each graphic symbol. The Amharic language is spoken by more than 57 million people with up to 32 million native speakers and 25 million non-native speakers \cite{eberhard2020ethnologue}. Amharic is the working language of the Federal Democratic Republic of Ethiopia (FDRE) and for many regional states in the country. In Amharic, there are 34 core characters each having seven different derivatives to represent vowels. In addition, it has 20 labialized characters, more than 20 numerals, and 8 punctuation marks. Amharic uses a total of more than 310 characters. The language is known for being morphologically complex and it is highly inflectional. Unlike English, French, Spanish, Japanese, and Chinese, Amharic is considered low-resource because the data are not well organized and technologically less supported \cite{woldeyohannis2017experimenting}.

 %
\section{Motivation}\label{motivation}
Nowadays advancement in technology has made the lifestyle of human beings much easier by helping daily activities. One of the applications that solved communication barriers between people speaking different languages is machine translation. Many big technology companies such as Google, Microsoft, IBM, etc. provide translation services for many languages to facilitate communication between people without using a human translator. However, the quality of NMT is massively dependent on quantity, quality, and relevance of the training dataset \cite{ahmadnia2019augmenting}. Such companies have achieved promising results for bilingual high-resource languages, but they are inadequate for low-resource languages like Amharic.

\begin{figure}[ht!]
    \centering
    \includegraphics[width=0.7\linewidth]{images/GMT.png}
   \caption{Examples of Google Amharic to English translation}
    \label{fig:motiv}
\end{figure}
Figure \ref{fig:motiv} shows Google translation of Amharic words into English. Google translated the Amharic input as "stomach empty"  which is a wrong translation. The correct literal meaning will be "he become Disappointed". This shows that the applications used by companies that provide translation system like Google require improvements. One of the cause for poor performance of MT systems for languages like Amharic is availability of limited resource in digital space \cite{ahmadnia2019augmenting}. In this research work we present a newly curated English-Amharic parallel dataset that can be used for MT research and help to solve the performance issue of an Amharic MT system.

On the other hand, Amharic is one of morphologically rich language and normalization of the Amharic homophone characters might have an impact on such downstream NLP applications as MT and sentiment analysis \cite{belay2021impacts}. This research work is intended to study the effect of homophone normalization on Amharic-English machine translation. Furthermore, expanding the translation dataset and developing state-of-the-art bi-directional Amharic-English translation models are our motivations to carryout this research work.

\section{Related work} \label{related_work}
Many automatic translation works have been carried out for the major pairs of European and Asian languages, taking advantage of large-scale parallel corpora. However, very few research studies have been conducted on low-resource languages like Amharic to English due to its scarcity of parallel data. In this section, we have focused on exploring how machine translation is conducted for the Amharic language.
Among recent works, Biadgligne and Smaïli  \cite{biadgligne2021parallel} described the development of an English-Amharic Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) experiments, achieving 26.47 and 32.44 BLEU scores, respectively. They harvested and used 225,304 parallel sentences in different domains. To the best of our knowledge, Biadgligne and Smaïli  \cite{biadgligne2021parallel} work is the largest dataset used in Amharic machine translation research work.

Gezmu et al. \cite{gezmu2021extended} trained neural machine translation and phrase-based statistical machine translation models using 145,364 parallel English-Amharic sentences, achieved a BLEU score of 20.2 and 26.6 respectively. They concluded that neural machine translation models outperform phrase-based statistical machine translation models.
Abate et al. \cite{abate2018parallel} focused on the development of parallel corpora for English and five Ethiopian languages, including Amharic. They used 40,726 parallel sentences for Amharic and bi-directional Statistical Machine Translation (SMT) technique. Finally, they achieved a BLEU score 22.68  in Amharic-English translation.

Teshome et al. \cite{teshome2015phoneme} considered the application of phone-based statistical machine translation for English-Amharic translation. They used 18,432 parallel sentences and achieved a BLEU score of 37.5, a gain of 2.21 BLEU point from their own previous baseline phrase-based MT experiments.
Further more, Tracey and Strassel \cite{tracey2020basic} developed a low-resource language dataset for emergent incidents (LORELEI). Their data includes multiple languages and was published in the Linguistic Data Consortium (LDC) where Amharic is among the languages with 60,884 Amharic-English parallel sentences. However, this dataset is not freely available for further experiments.

The remain and addressed related works are summarized in Table \ref{tab:table1}, with dataset and methods used. As we can see in this related work table, only a few studies have been conducted for the translation from Amharic into English or vice versa and most of them were conducted using traditional approaches with a small number of parallel sentences. This is due to unavailability of enough Amharic linguistic resources for deep learning experiments.

\begin{table*}[!ht]
\begin{center}
\caption{Amharic-English and English-Amharic MT studies in terms of dataset size,  method(s) used, and BLEU score achieved}
\label{tab:table1}
\begin{tabular}{@{}lllll@{}}
\hline
\textbf {Authors} & \textbf {Trans. direction}& \textbf {\# Dataset used} &  \textbf {Method(s)} & \textbf {BLEU score}\\
\hline
Biadgligne and Smaïli \cite{biadgligne2021parallel} & {En→Am} & 225,304 &   Statistical machine translation & 26.47 \\
& &  & Neural machine translation & 32.44 \\
\hline
Gezmu et al. \cite{gezmu2021extended} & Am→En & 45,364 & Phrase-based statistical machine translation  & 20.2 \\
& &  & Neural Machine Translation & 26.6 \\
\hline
Abate et al. \cite{abate2018parallel} & Am→En & 40,726 & Statistical machine translation & 22.68 \\
 &En→Am &  & Statistical machine translation & 13.31 \\
\hline
Teshome et al. \cite{teshome2015phoneme} & En→Am & 18,432 & Phoneme-based statistical machine translation & 37.5 \\
\hline
Teshome and Besacier \cite{teshome2012preliminary} & En→Am & 18,432 & Phrase-based statistical machine translation & 35.32 \\
\hline
Ashengo et al. \cite{ashengo2021context} & En→Am & 8,603 & Combination of context-based MT (CBMT) with RNN & 11.34 \\
\hline
Tadesse and Mekuria. \cite{tadesse2000} & En→Am & 37,970 & Statistical machine translation & 18.74\\
\hline
Hadgu et al. \cite{hadgu2020evaluating} & Am→En & 977 & Google translate, Yandex translate  & 23.2, 4.8 \\
& En→Am & 1915 & Google translate, Yandex translate & 9.6, 1.3 \\
\hline
\end{tabular}
\end{center}
\end{table*}
\section{Building a Parallel Dataset} \label{dataset}
In this section, we have identified available data sources that we have used for the research work. As machine translation requires parallel documents as an input, Table \ref{tab:table2} shows the potential Amharic-English bi-lingual resources. It can be seen in the table that the largest parallel corpus for Amharic-English language pairs was collected by Biadgligne and Smaïli \cite{biadgligne:hal-03547539}. In addition to the available datasets in Table \ref{tab:table2}, we have contributed to the MT research field by creating a new parallel corpus with 33,955 sentence pairs extracted text from such news platforms as Ethiopian Press Agency\footnote{\href {https://www.press.et/} {https://www.press.et/}}, Fana Broadcasting Corporate\footnote{\href {https://www.fanabc.com/} {https://www.fanabc.com/}}, and  Walta Information Center\footnote{\href {https://waltainfo.com/} {https://waltainfo.com/}}. As the data we used is from different sources, it includes various domains such as religious (Bible and Quran), politics, economics, sports, news, among others.

As one can see in Table \ref{tab:table2}, the total number of parallel sentences is about 1.1M, while the unique parallel sentences are 888,837. This is due to duplication in the sources we used. This unique parallel sentence is the largest to date then.
\section{Data pre-processing}\label{data_pre}
Before performing experiments, the development of every NLP task begins with a text pre-processing step \cite{kumar2012text}. As our data were collected from different sources, we noticed a lot of text irregularities. Some data was too noisy so we eliminated it from our corpus. Then we performed a series of pre-processing steps to canonize all tokens in Amharic and English sentences. In these steps, the following tasks were performed: data cleaning (removing emojis, URL), abbreviation expansion, Latin character lowercase, duplicated sentence removal, and Amharic homophone character normalization. For abbreviation expansion, we created a list of known abbreviations for both Amharic and English, then expanded them to their full writing form. Most of English abbreviations were collected from GitHub repositories\footnote{\url{https://github.com/JRC1995/Machine-Translation-Transformers}}.
\begin{table}[H]
\centering
\caption{\label{tab:table2}Available Amharic-English parallel data sources}
\begin{tabular}{lcc}
\hline
\textbf{Data source} & \textbf{\# Sentence pairs} & \textbf{Accessible}\\
\hline
Am-En ELRA-W0074\tablefootnote{\url{ http://catalog.elra.info/en-us/repository/browse/ELRA-W0074/}}
 & 13,347 & yes\\%2,814,888 4,068,831

Biadgligne and Smaïli\cite{biadgligne2021parallel
 & 225,304 & yes\\

Horn MT\tablefootnote{\url{https://github.com/asmelashteka/HornMT}} & 2,030 & yes\\

Am-En MT corpus\tablefootnote{\url{https://github.com/adtsegaye/Amharic-English-Machine-Translation-Corpus}} & 53,312 & yes\\

Gezmu et al.\cite{gezmu2021extended
& 145,364 & yes\\

Abate et al.\cite{abate2018parallel
& 40,726 & yes\\

Open Parallel Corpus (OPUS) \cite{lison2016opensubtitles2016} & 562,141 & yes\\

LORELEI-Amharic \cite{tracey2020basic
& 60,884 & no\\

Admasethiopia\tablefootnote{\url{https://github.com/admasethiopia/parallel-text}} & 153 & yes\\

MT Evaluation Dataset\tablefootnote{\url{https://doi.org/10.5281/zenodo.3734260}} & 2,914 & yes\\

\textbf{Newly curated (our data)} & \textbf{33,955} & \textbf{yes}\\
\hline
\textbf{Total} & 1,140,130 & yes\\
\textbf{Unique sentence pairs} & 888,837 & yes\\
\hline
\end{tabular}
\end{table}
\textbf{Homophone character normalization}:
In Amharic writing, there are different characters with the same sound which are called homophones. Homophones with different symbols in Amharic text might have different writing standards and different meanings. However, they are also considered as redundant alphabets by most of the users, specially by the online and social media communities.

The current trend in Amharic NLP research is to normalize the homophone characters into a single representation \cite{woldeyohannis2017experimenting,abate2018parallel,gezmu2021extended,biadgligne2021parallel}. Belay et al. \cite{belay2021impacts} have studied the impact of normalization for some downstreamm NLP applications. They showed that homophone normalization improves the performance of some downstream applications such as information retrieval (IR) but not recommended for sentiment analysis. However, there is no study to show that normalization helps to build efficient machine translation models or not. We developed our translation models in the regular (unnormalized) and the normalized forms (representing different homophones as a single character) to analyze the impact of normalization. For the normalized model, we used frequency-based homophone normalizer \cite{belay2021impacts} replacing the set of characters with similar function with a single most frequently used character.

\begin{figure}[hbt!]
    \centering
    \includegraphics[width=\linewidth]{images/homophone.png}
   \caption{Amharic homophone characters with their 7 derivatives using their International Phonetic Alphabet (IPA) notation. The normalized homophones is the first one in boldface in cell of each row except the  of \nth{4} column of the \nth{1} and \nth{2} row where the \nth{1} character was used as the normalized one.}
    \label{fig:homophone}
\end{figure}

\section{The proposed neural machine translation models}\label{models}
In this section, we describe the transformer-based pre-trained models that could be used to train Neural Machine Translation systems.
\begin{comment}
\subsection{Transformers}
\begin{figure}[hbt!]
    \centering
    \includegraphics[width=0.5\linewidth]{images/transformer.jpg}
   \caption{Overview of the structure of the Transformer network, adopted from Vaswani et al. \cite{vaswani2017attention}.}
    \label{fig:trans}
\end{figure}
\end{comment}
Unlike other neural networks such as RNNs, the Transformer does not necessarily process the input data in sequential order. Instead, the self-attention mechanism identifies the context which gives meaning to each position in the input sequence, allowing more parallelization, and reducing the training time. The architecture of the Transformer network follows the so called encoder-decoder paradigm, trained in an end-to-end fashion. The encoder is used to represent the source sentence as a semantic vector, while the decoder takes in the semantic vector and makes prediction to a target sentence.
The Transformer model, which applies a self-attention approach to measure the strength of a relationship between two words in a sentence (contextual information), has contributed to improving performance in MT and various natural language processing tasks.

\subsection{Pre-trained language models (PLMs)}
Pre-trained Language Models (PLMs) are large neural networks used in a wide variety of NLP tasks \cite{elazar2021measuring}. Models are first pre-trained over a large text corpus and then fine-tuned on a downstream task. PLMs are thought of as good language encoders, supplying basic language understanding capabilities that can be used with ease for many downstream tasks \cite{elazar2021measuring,weng2020acquiring}.

Fan et al. \cite{fan2020beyond}, Facebook researchers, proposed a multilingual encoder-decoder model trained for Many-to-Many multilingual translation with 418M parameters (M2M100 418M). This multilingual machine translation model is based on the Transformer sequence-to-sequence architecture. The model can directly translate between the 9,900 directions of 100 languages. We used this pre-trained model for our bi-directional translation experiments. This pre-trained multi-lingual model is available at Hugging Face\footnote{\url{https://huggingface.co/facebook/m2m100_418M}}

\section{Experimental setup and results}\label{experiment}
\subsection{Experimental setup}
To demonstrate the effectiveness of our approach, we built our baseline Transformer models and fine-tuned the available pre-trained model. We used Google colab pro+ to train our bi-directional Amharic to English translation models. During model training, the parallel sentences for Amharic and English were divided into 80\% for training, 10\% validation, and 10\% for testing. Automatic evaluation was made using Bilingual Evaluation Understudy (BLEU) metric \cite{papineni2002bleu}. BLEU score is defined in the range between 0 and 1, where 1 is a perfect match with the reference and 0 is for no words matched.

\textbf{Transformer baseline:} We trained Transformer sequence-to-sequence models from scratch for bi-directional Amharic-English NMT using OpenNMT with TensorFlow deep learning framework \cite{klein2017opennmt}. We tokenized the text using Byte Pair Encoding (BPE) \cite{gage1994new} subword tokenization, which is a simple form of data compression algorithm in which the most common pair of consecutive bytes of data is replaced with a byte that does not occur in that data. The parameters used to train the model are 512 hidden units, 6 layers, a learning rate of 0.0001, and a maximum step of 50K, a batch size of 32, and the Adam optimizer.

\textbf{Pre-trained model:} We used the multilingual Facebook M2M-100 pre-trained model with 418M parameters \cite{fan2020beyond} to fine-tune into bi-directional Amharic-English NMT. For fine-tuning, we used max source \& target length of 128 per device train and validation, a batch size of 4, and 3 epochs.

\subsection{Results and discussion}
In our experiments, we built Transformer models and fine-tuned M2M100\_418M, the multi-lingual pre-trained language model for bi-directional Amharic-English translation. All models were built on a regular (unnormalized) and normalized datasets to study the effect of Amharic homophone normalization in Amharic-English translation. Table \ref{tab:table3} shows the experimental results of Transformer and PLMs of M2M100 in both directions.
\begin{table}[h!]
\centering
\caption{\label{tab:table3}
Experimental results}
\begin{tabular}{lcc}
\hline
\textbf{Models} & \textbf{Regular}& \textbf{Normalized}\\
\hline
Transformer (Amharic→English) & 14.78 & 16.26 \\
Transformer (English→Amharic) & 10.79 & 13.06 \\
M2M100 48M (Amharic→English) & 34.12 & \textbf{37.79} \\
M2M100 48M (English→Amharic) & 29.65 & \textbf{32.74} \\
\hline
\end{tabular}
\end{table}

As it can be observed from the results in Table \ref{tab:table3}, the multilingual translation model M2M-100 outperforms the Transformer-based models in both directions. When we compare our results with the attempts mentioned in Section \ref{related_work}, our research shows an improvement in parallel corpus size and BLEU scores using multilingual translation model. We can evaluate our models in the translation directions as follows.

\textbf{Amharic-English Translation:}
 For both Amharic-English and English-Amharic translations, the Transformer models did not work well, performed even less than some traditional methods.
 The pre-trained transformer-based model performed better with 34.12 and 37.79 BLEU score for Amharic-English translation, on the regular and normalized data, respectively. This clearly shows that pre-trained based translation outperforms the baseline system.

\textbf{English-Amharic Translation:}
Among the works conducted for English-Amharic translation, the work by Teshome et al. \cite{teshome2015phoneme} reached a BLEU score of 37.53 using phoneme-based statistical MT, while our  pre-trained model showed a BLEU score of 29.65 and 32.74 on regular and normalized data, respectively. This phoneme-based work \cite{teshome2015phoneme} is built by converting the Amharic syllables to phoneme-based characters; however 1) the conversion is rule-based (unable to handle all exceptions), 2) Amharic homophones do not have constant Latin representations, and 3) we used new train and test sets. So, the results are not comparable. Another insight from this work is pre-trained model still needs improvement to translate from technologically favored languages to languages like Amharic which is morphologically rich.
\begin{figure}[ht!]
    \centering
    \includegraphics[width=\linewidth]{images/result.PNG}
   \caption{Reporting of our work result with other previous works}
    \label{fig:result}
\end{figure}

Our results also clearly show the effect of homophone character normalization in the performance of bi-directional Amharic-English translation. For both (Transformer and pre-trained) models, homophone character normalization increased the performance of NMT system.
Accordingly, comparing our results with the previous attempts as shown in Figure \ref{fig:result}, we can confirm that our models demonstrated a 0.29\% BLEU score increase. This BLEU score improvement was made using our new training and testing sets. This is because, first, all the mentioned MT data is not available; second, the available data is not split into constant training and testing categories for future evaluation purposes. For the next time, our dataset will be available in train, test, and validation files.

\section{Conclusion and future work}\label{conclusion}
In this paper, we presented our Amharic-English parallel corpus and bi-directional machine translation experiments. We gathered more than 888K parallel unique sentences and applied different pre-processing techniques. This is the first large-scale parallel data (more than 5 times bigger than the data in the previously conducted MT works) which can be used as a good benchmark for future machine translation research. The main gaps from the previous work were that they used small data and were not ready for future comparison (benchmark) by splitting the data into constant train, test, and validation sets. Our work will solve this issue and be used as a benchmark.
According to our result, we can conclude that the pre-trained models outperformed the baseline Transformer-based model. The morphological richness of the Amharic language and the size of the parallel dataset has a great impact on Amharic-English MT experiments.
For the future, we will expand this work for more languages by including other Ethiopian low-resourced languages and also use data augmentation techniques. Additionally, we plan to explore the applicability of other pre-trained language models for Amharic-English translations.
Our dataset of parallel Amharic-English sentence pairs, the models, and pre-processing scripts will be released in the GitHub repository\footnote{https://github.com/atnafuatx/EthioNMT-datasets}
\bibliographystyle{IEEEtran}

\section{Introduction}
Machine translation (MT) is a  sub-field of natural language processing (NLP) that investigates how to use computer software to automatically translate text or speech from one language to another without human involvement.
MT is one of the prominent tasks in NLP that is tackled in several ways \cite{bojar2017findings}. The first MT research began at about 1950s and in 1952 the first International Conference on Machine Translation was organized at the Massachusetts Institute of Technology (MIT). It has long research history and experienced four stages, namely, Rule-based MT \cite{forcada2011apertium},  Statistical MT (SMT) \cite{koehn2007moses}, hybrid MT, and Neural MT (NMT) \cite{cho2014properties,kalchbrenner2013recurrent}.

The most severe drawback of the rule-based method is that it
has ignored the need for context information in the translation
process. It is highly dependent on hand-crafted features. Phrase-based SMT (PBSMT), the most prevalent version of SMT, generates translation by segmenting the source sentence into several phrases and performing phrase translation and replacement. It may ignore the long sentence dependency and require high computing devices \cite{baniata2021transformer}. Recently, using deep neural networks for MT task has received great attention. NMT also improves training procedures due to the end-to-end procedure without tedious feature engineering and complex setups. NMT employs such techniques as recurrent neural network (RNN) \cite{bahdanau2014neural}, convolutional neural network (CNN) \cite{gehring2016convolutional}, and self-attention network (Transformer) \cite{vaswani2017attention}.

Transformer models with the pre-training approach is a new NMT strategy entirely based on attention mechanisms proposed in 2017 \cite{vaswani2017attention}. Among the different neural network architectures, the Transformer model has emerged as the dominant NMT paradigm \cite{raganato2018analysis,yigezu2021multilingual, tonja2021parallel}. It has become the state-of-the-art model for many artificial intelligence tasks, including machine translation. In terms of model, the Transformer-based pre-trained models are fast to fine-tune, highly accurate and has been proven to outperform widely used recurrent networks \cite{zhang2018improving,baniata2021transformer,yang2020survey}.

The focus of MT research for the Amharic language has been on rule-based and SMT methods. In this work, we used the transformer model as a baseline translation system to explore the applicability of Facebook M2M100 multi-lingual pre-trained language model for Amharic-English translation in both directions. Furthermore, this research work investigated the impact of normalization of the Amharic homophones on Amharic-English MT tasks.

The main contributions of this work are:
\begin{enumerate}
    \item Exploration of the Amharic-English and English-Amharic machine translation tasks.
    \item Introduction of the first large-scale publicly available Amharic-English translation parallel dataset.
    \item Development and implementation of state-of-the-art Amharic-English translation models.
    \item Investigation of the effect of Amharic homophone character normalization on the machine translation task.
\end{enumerate}

The rest of this paper is organized as follows. Section \ref{amharic_langauge} presents a detail description of Amharic language while Section \ref{motivation} shows the motivation for this research. In Section \ref{related_work}, we review related work. Section \ref{dataset} describes the existing parallel corpus and the collection of a new corpus from the news domain. The general pre-processing steps applied to both corpora are presented in Section \ref{data_pre}. Section \ref{models} discusses the proposed NMT models and Section \ref{experiment} gives the experimental results. In the end, Section \ref{conclusion} concludes the paper and sheds some light on possible future work.

\section{Amharic language}\label{amharic_langauge}
 Amharic is the second most spoken Semitic language next to Arabic which has its own alphabet and writing scripts called 'Fidel', that was borrowed from Ge'ez, another Ethiopian Semitic language.  Fidel is a syllable-based writing system where the consonants and vowels co-exist within each graphic symbol. The Amharic language is spoken by more than 57 million people with up to 32 million native speakers and 25 million non-native speakers \cite{eberhard2020ethnologue}. Amharic is the working language of the Federal Democratic Republic of Ethiopia (FDRE) and for many regional states in the country. In Amharic, there are 34 core characters each having seven different derivatives to represent vowels. In addition, it has 20 labialized characters, more than 20 numerals, and 8 punctuation marks. Amharic uses a total of more than 310 characters. The language is known for being morphologically complex and it is highly inflectional. Unlike English, French, Spanish, Japanese, and Chinese, Amharic is considered low-resource because the data are not well organized and technologically less supported \cite{woldeyohannis2017experimenting}.

 %
\section{Motivation}\label{motivation}
Nowadays advancement in technology has made the lifestyle of human beings much easier by helping daily activities. One of the applications that solved communication barriers between people speaking different languages is machine translation. Many big technology companies such as Google, Microsoft, IBM, etc. provide translation services for many languages to facilitate communication between people without using a human translator. However, the quality of NMT is massively dependent on quantity, quality, and relevance of the training dataset \cite{ahmadnia2019augmenting}. Such companies have achieved promising results for bilingual high-resource languages, but they are inadequate for low-resource languages like Amharic.

\begin{figure}[ht!]
    \centering
    \includegraphics[width=0.7\linewidth]{images/GMT.png}
   \caption{Examples of Google Amharic to English translation}
    \label{fig:motiv}
\end{figure}
Figure \ref{fig:motiv} shows Google translation of Amharic words into English. Google translated the Amharic input as "stomach empty"  which is a wrong translation. The correct literal meaning will be "he become Disappointed". This shows that the applications used by companies that provide translation system like Google require improvements. One of the cause for poor performance of MT systems for languages like Amharic is availability of limited resource in digital space \cite{ahmadnia2019augmenting}. In this research work we present a newly curated English-Amharic parallel dataset that can be used for MT research and help to solve the performance issue of an Amharic MT system.

On the other hand, Amharic is one of morphologically rich language and normalization of the Amharic homophone characters might have an impact on such downstream NLP applications as MT and sentiment analysis \cite{belay2021impacts}. This research work is intended to study the effect of homophone normalization on Amharic-English machine translation. Furthermore, expanding the translation dataset and developing state-of-the-art bi-directional Amharic-English translation models are our motivations to carryout this research work.

\section{Related work} \label{related_work}
Many automatic translation works have been carried out for the major pairs of European and Asian languages, taking advantage of large-scale parallel corpora. However, very few research studies have been conducted on low-resource languages like Amharic to English due to its scarcity of parallel data. In this section, we have focused on exploring how machine translation is conducted for the Amharic language.
Among recent works, Biadgligne and Smaïli  \cite{biadgligne2021parallel} described the development of an English-Amharic Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) experiments, achieving 26.47 and 32.44 BLEU scores, respectively. They harvested and used 225,304 parallel sentences in different domains. To the best of our knowledge, Biadgligne and Smaïli  \cite{biadgligne2021parallel} work is the largest dataset used in Amharic machine translation research work.

Gezmu et al. \cite{gezmu2021extended} trained neural machine translation and phrase-based statistical machine translation models using 145,364 parallel English-Amharic sentences, achieved a BLEU score of 20.2 and 26.6 respectively. They concluded that neural machine translation models outperform phrase-based statistical machine translation models.
Abate et al. \cite{abate2018parallel} focused on the development of parallel corpora for English and five Ethiopian languages, including Amharic. They used 40,726 parallel sentences for Amharic and bi-directional Statistical Machine Translation (SMT) technique. Finally, they achieved a BLEU score 22.68  in Amharic-English translation.

Teshome et al. \cite{teshome2015phoneme} considered the application of phone-based statistical machine translation for English-Amharic translation. They used 18,432 parallel sentences and achieved a BLEU score of 37.5, a gain of 2.21 BLEU point from their own previous baseline phrase-based MT experiments.
Further more, Tracey and Strassel \cite{tracey2020basic} developed a low-resource language dataset for emergent incidents (LORELEI). Their data includes multiple languages and was published in the Linguistic Data Consortium (LDC) where Amharic is among the languages with 60,884 Amharic-English parallel sentences. However, this dataset is not freely available for further experiments.

The remain and addressed related works are summarized in Table \ref{tab:table1}, with dataset and methods used. As we can see in this related work table, only a few studies have been conducted for the translation from Amharic into English or vice versa and most of them were conducted using traditional approaches with a small number of parallel sentences. This is due to unavailability of enough Amharic linguistic resources for deep learning experiments.

\begin{table*}[!ht]
\begin{center}
\caption{Amharic-English and English-Amharic MT studies in terms of dataset size,  method(s) used, and BLEU score achieved}
\label{tab:table1}
\begin{tabular}{@{}lllll@{}}
\hline
\textbf {Authors} & \textbf {Trans. direction}& \textbf {\# Dataset used} &  \textbf {Method(s)} & \textbf {BLEU score}\\
\hline
Biadgligne and Smaïli \cite{biadgligne2021parallel} & {En→Am} & 225,304 &   Statistical machine translation & 26.47 \\
& &  & Neural machine translation & 32.44 \\
\hline
Gezmu et al. \cite{gezmu2021extended} & Am→En & 45,364 & Phrase-based statistical machine translation  & 20.2 \\
& &  & Neural Machine Translation & 26.6 \\
\hline
Abate et al. \cite{abate2018parallel} & Am→En & 40,726 & Statistical machine translation & 22.68 \\
 &En→Am &  & Statistical machine translation & 13.31 \\
\hline
Teshome et al. \cite{teshome2015phoneme} & En→Am & 18,432 & Phoneme-based statistical machine translation & 37.5 \\
\hline
Teshome and Besacier \cite{teshome2012preliminary} & En→Am & 18,432 & Phrase-based statistical machine translation & 35.32 \\
\hline
Ashengo et al. \cite{ashengo2021context} & En→Am & 8,603 & Combination of context-based MT (CBMT) with RNN & 11.34 \\
\hline
Tadesse and Mekuria. \cite{tadesse2000} & En→Am & 37,970 & Statistical machine translation & 18.74\\
\hline
Hadgu et al. \cite{hadgu2020evaluating} & Am→En & 977 & Google translate, Yandex translate  & 23.2, 4.8 \\
& En→Am & 1915 & Google translate, Yandex translate & 9.6, 1.3 \\
\hline
\end{tabular}
\end{center}
\end{table*}
\section{Building a Parallel Dataset} \label{dataset}
In this section, we have identified available data sources that we have used for the research work. As machine translation requires parallel documents as an input, Table \ref{tab:table2} shows the potential Amharic-English bi-lingual resources. It can be seen in the table that the largest parallel corpus for Amharic-English language pairs was collected by Biadgligne and Smaïli \cite{biadgligne:hal-03547539}. In addition to the available datasets in Table \ref{tab:table2}, we have contributed to the MT research field by creating a new parallel corpus with 33,955 sentence pairs extracted text from such news platforms as Ethiopian Press Agency\footnote{\href {https://www.press.et/} {https://www.press.et/}}, Fana Broadcasting Corporate\footnote{\href {https://www.fanabc.com/} {https://www.fanabc.com/}}, and  Walta Information Center\footnote{\href {https://waltainfo.com/} {https://waltainfo.com/}}. As the data we used is from different sources, it includes various domains such as religious (Bible and Quran), politics, economics, sports, news, among others.

As one can see in Table \ref{tab:table2}, the total number of parallel sentences is about 1.1M, while the unique parallel sentences are 888,837. This is due to duplication in the sources we used. This unique parallel sentence is the largest to date then.
\section{Data pre-processing}\label{data_pre}
Before performing experiments, the development of every NLP task begins with a text pre-processing step \cite{kumar2012text}. As our data were collected from different sources, we noticed a lot of text irregularities. Some data was too noisy so we eliminated it from our corpus. Then we performed a series of pre-processing steps to canonize all tokens in Amharic and English sentences. In these steps, the following tasks were performed: data cleaning (removing emojis, URL), abbreviation expansion, Latin character lowercase, duplicated sentence removal, and Amharic homophone character normalization. For abbreviation expansion, we created a list of known abbreviations for both Amharic and English, then expanded them to their full writing form. Most of English abbreviations were collected from GitHub repositories\footnote{\url{https://github.com/JRC1995/Machine-Translation-Transformers}}.
\begin{table}[H]
\centering
\caption{\label{tab:table2}Available Amharic-English parallel data sources}
\begin{tabular}{lcc}
\hline
\textbf{Data source} & \textbf{\# Sentence pairs} & \textbf{Accessible}\\
\hline
Am-En ELRA-W0074\tablefootnote{\url{ http://catalog.elra.info/en-us/repository/browse/ELRA-W0074/}}
 & 13,347 & yes\\%2,814,888 4,068,831

Biadgligne and Smaïli\cite{biadgligne2021parallel
 & 225,304 & yes\\

Horn MT\tablefootnote{\url{https://github.com/asmelashteka/HornMT}} & 2,030 & yes\\

Am-En MT corpus\tablefootnote{\url{https://github.com/adtsegaye/Amharic-English-Machine-Translation-Corpus}} & 53,312 & yes\\

Gezmu et al.\cite{gezmu2021extended
& 145,364 & yes\\

Abate et al.\cite{abate2018parallel
& 40,726 & yes\\

Open Parallel Corpus (OPUS) \cite{lison2016opensubtitles2016} & 562,141 & yes\\

LORELEI-Amharic \cite{tracey2020basic
& 60,884 & no\\

Admasethiopia\tablefootnote{\url{https://github.com/admasethiopia/parallel-text}} & 153 & yes\\

MT Evaluation Dataset\tablefootnote{\url{https://doi.org/10.5281/zenodo.3734260}} & 2,914 & yes\\

\textbf{Newly curated (our data)} & \textbf{33,955} & \textbf{yes}\\
\hline
\textbf{Total} & 1,140,130 & yes\\
\textbf{Unique sentence pairs} & 888,837 & yes\\
\hline
\end{tabular}
\end{table}
\textbf{Homophone character normalization}:
In Amharic writing, there are different characters with the same sound which are called homophones. Homophones with different symbols in Amharic text might have different writing standards and different meanings. However, they are also considered as redundant alphabets by most of the users, specially by the online and social media communities.

The current trend in Amharic NLP research is to normalize the homophone characters into a single representation \cite{woldeyohannis2017experimenting,abate2018parallel,gezmu2021extended,biadgligne2021parallel}. Belay et al. \cite{belay2021impacts} have studied the impact of normalization for some downstreamm NLP applications. They showed that homophone normalization improves the performance of some downstream applications such as information retrieval (IR) but not recommended for sentiment analysis. However, there is no study to show that normalization helps to build efficient machine translation models or not. We developed our translation models in the regular (unnormalized) and the normalized forms (representing different homophones as a single character) to analyze the impact of normalization. For the normalized model, we used frequency-based homophone normalizer \cite{belay2021impacts} replacing the set of characters with similar function with a single most frequently used character.

\begin{figure}[hbt!]
    \centering
    \includegraphics[width=\linewidth]{images/homophone.png}
   \caption{Amharic homophone characters with their 7 derivatives using their International Phonetic Alphabet (IPA) notation. The normalized homophones is the first one in boldface in cell of each row except the  of \nth{4} column of the \nth{1} and \nth{2} row where the \nth{1} character was used as the normalized one.}
    \label{fig:homophone}
\end{figure}

\section{The proposed neural machine translation models}\label{models}
In this section, we describe the transformer-based pre-trained models that could be used to train Neural Machine Translation systems.
\begin{comment}
\subsection{Transformers}
\begin{figure}[hbt!]
    \centering
    \includegraphics[width=0.5\linewidth]{images/transformer.jpg}
   \caption{Overview of the structure of the Transformer network, adopted from Vaswani et al. \cite{vaswani2017attention}.}
    \label{fig:trans}
\end{figure}
\end{comment}
Unlike other neural networks such as RNNs, the Transformer does not necessarily process the input data in sequential order. Instead, the self-attention mechanism identifies the context which gives meaning to each position in the input sequence, allowing more parallelization, and reducing the training time. The architecture of the Transformer network follows the so called encoder-decoder paradigm, trained in an end-to-end fashion. The encoder is used to represent the source sentence as a semantic vector, while the decoder takes in the semantic vector and makes prediction to a target sentence.
The Transformer model, which applies a self-attention approach to measure the strength of a relationship between two words in a sentence (contextual information), has contributed to improving performance in MT and various natural language processing tasks.

\subsection{Pre-trained language models (PLMs)}
Pre-trained Language Models (PLMs) are large neural networks used in a wide variety of NLP tasks \cite{elazar2021measuring}. Models are first pre-trained over a large text corpus and then fine-tuned on a downstream task. PLMs are thought of as good language encoders, supplying basic language understanding capabilities that can be used with ease for many downstream tasks \cite{elazar2021measuring,weng2020acquiring}.

Fan et al. \cite{fan2020beyond}, Facebook researchers, proposed a multilingual encoder-decoder model trained for Many-to-Many multilingual translation with 418M parameters (M2M100 418M). This multilingual machine translation model is based on the Transformer sequence-to-sequence architecture. The model can directly translate between the 9,900 directions of 100 languages. We used this pre-trained model for our bi-directional translation experiments. This pre-trained multi-lingual model is available at Hugging Face\footnote{\url{https://huggingface.co/facebook/m2m100_418M}}

\section{Experimental setup and results}\label{experiment}
\subsection{Experimental setup}
To demonstrate the effectiveness of our approach, we built our baseline Transformer models and fine-tuned the available pre-trained model. We used Google colab pro+ to train our bi-directional Amharic to English translation models. During model training, the parallel sentences for Amharic and English were divided into 80\% for training, 10\% validation, and 10\% for testing. Automatic evaluation was made using Bilingual Evaluation Understudy (BLEU) metric \cite{papineni2002bleu}. BLEU score is defined in the range between 0 and 1, where 1 is a perfect match with the reference and 0 is for no words matched.

\textbf{Transformer baseline:} We trained Transformer sequence-to-sequence models from scratch for bi-directional Amharic-English NMT using OpenNMT with TensorFlow deep learning framework \cite{klein2017opennmt}. We tokenized the text using Byte Pair Encoding (BPE) \cite{gage1994new} subword tokenization, which is a simple form of data compression algorithm in which the most common pair of consecutive bytes of data is replaced with a byte that does not occur in that data. The parameters used to train the model are 512 hidden units, 6 layers, a learning rate of 0.0001, and a maximum step of 50K, a batch size of 32, and the Adam optimizer.

\textbf{Pre-trained model:} We used the multilingual Facebook M2M-100 pre-trained model with 418M parameters \cite{fan2020beyond} to fine-tune into bi-directional Amharic-English NMT. For fine-tuning, we used max source \& target length of 128 per device train and validation, a batch size of 4, and 3 epochs.

\subsection{Results and discussion}
In our experiments, we built Transformer models and fine-tuned M2M100\_418M, the multi-lingual pre-trained language model for bi-directional Amharic-English translation. All models were built on a regular (unnormalized) and normalized datasets to study the effect of Amharic homophone normalization in Amharic-English translation. Table \ref{tab:table3} shows the experimental results of Transformer and PLMs of M2M100 in both directions.
\begin{table}[h!]
\centering
\caption{\label{tab:table3}
Experimental results}
\begin{tabular}{lcc}
\hline
\textbf{Models} & \textbf{Regular}& \textbf{Normalized}\\
\hline
Transformer (Amharic→English) & 14.78 & 16.26 \\
Transformer (English→Amharic) & 10.79 & 13.06 \\
M2M100 48M (Amharic→English) & 34.12 & \textbf{37.79} \\
M2M100 48M (English→Amharic) & 29.65 & \textbf{32.74} \\
\hline
\end{tabular}
\end{table}

As it can be observed from the results in Table \ref{tab:table3}, the multilingual translation model M2M-100 outperforms the Transformer-based models in both directions. When we compare our results with the attempts mentioned in Section \ref{related_work}, our research shows an improvement in parallel corpus size and BLEU scores using multilingual translation model. We can evaluate our models in the translation directions as follows.

\textbf{Amharic-English Translation:}
 For both Amharic-English and English-Amharic translations, the Transformer models did not work well, performed even less than some traditional methods.
 The pre-trained transformer-based model performed better with 34.12 and 37.79 BLEU score for Amharic-English translation, on the regular and normalized data, respectively. This clearly shows that pre-trained based translation outperforms the baseline system.

\textbf{English-Amharic Translation:}
Among the works conducted for English-Amharic translation, the work by Teshome et al. \cite{teshome2015phoneme} reached a BLEU score of 37.53 using phoneme-based statistical MT, while our  pre-trained model showed a BLEU score of 29.65 and 32.74 on regular and normalized data, respectively. This phoneme-based work \cite{teshome2015phoneme} is built by converting the Amharic syllables to phoneme-based characters; however 1) the conversion is rule-based (unable to handle all exceptions), 2) Amharic homophones do not have constant Latin representations, and 3) we used new train and test sets. So, the results are not comparable. Another insight from this work is pre-trained model still needs improvement to translate from technologically favored languages to languages like Amharic which is morphologically rich.
\begin{figure}[ht!]
    \centering
    \includegraphics[width=\linewidth]{images/result.PNG}
   \caption{Reporting of our work result with other previous works}
    \label{fig:result}
\end{figure}

Our results also clearly show the effect of homophone character normalization in the performance of bi-directional Amharic-English translation. For both (Transformer and pre-trained) models, homophone character normalization increased the performance of NMT system.
Accordingly, comparing our results with the previous attempts as shown in Figure \ref{fig:result}, we can confirm that our models demonstrated a 0.29\% BLEU score increase. This BLEU score improvement was made using our new training and testing sets. This is because, first, all the mentioned MT data is not available; second, the available data is not split into constant training and testing categories for future evaluation purposes. For the next time, our dataset will be available in train, test, and validation files.

\section{Conclusion and future work}\label{conclusion}
In this paper, we presented our Amharic-English parallel corpus and bi-directional machine translation experiments. We gathered more than 888K parallel unique sentences and applied different pre-processing techniques. This is the first large-scale parallel data (more than 5 times bigger than the data in the previously conducted MT works) which can be used as a good benchmark for future machine translation research. The main gaps from the previous work were that they used small data and were not ready for future comparison (benchmark) by splitting the data into constant train, test, and validation sets. Our work will solve this issue and be used as a benchmark.
According to our result, we can conclude that the pre-trained models outperformed the baseline Transformer-based model. The morphological richness of the Amharic language and the size of the parallel dataset has a great impact on Amharic-English MT experiments.
For the future, we will expand this work for more languages by including other Ethiopian low-resourced languages and also use data augmentation techniques. Additionally, we plan to explore the applicability of other pre-trained language models for Amharic-English translations.
Our dataset of parallel Amharic-English sentence pairs, the models, and pre-processing scripts will be released in the GitHub repository\footnote{https://github.com/atnafuatx/EthioNMT-datasets}
\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


text:
Q: Add lightning component action to related list I need some help please! I know something can be done, I just can't find the answer to this.
I have these two objects Case (parent) and Party Document (child). One of my team's developers has created a lightning component action in Buttons, Links, and Actions in obj. manager for Party Document and they now need to show the action on the Party Document related list on Case, but I can't seem to figure that one out. You'll see in the image, when we click the down arrow, that's where we want it to show. Any help is appreciated! Thank you!

A: Unfortunately, what you are looking for is not a supported feature in Lightning at the moment (see idea link below). This is a painful limitation on the platform, but is not totally hopeless as you can still create list buttons and add to your lightning pages. The limitation here is that you have to decide whether to send the user to a URL, run some Javascript, or send them to a VF page.
Another option would be to rebuild the related list as an LWC, or I've seen others simply add a button just above the related list - not exactly elegant, but it does the trick.
Of course, take a look on the AppExchange before building anything. There are several free apps which provide additional customization for your related lists!
https://trailblazer.salesforce.com/ideaView?id=0873A0000003TXFQA2

redpajama_set_name: RedPajamaStackExchange


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