Text Embeddings for Retrieval from a Large Knowledge Base
Text embedding representing natural language documents in a semantic vector space can be used for document retrieval using nearest neighbor lookup. In order to study the feasibility of neural models specialized for retrieval in a semantically meaningful way, we suggest the use of the Stanford Question Answering Dataset (SQuAD) in an open-domain question answering context, where the first task is to find paragraphs useful for answering a given question. First, we compare the quality of various text-embedding methods on the performance of retrieval and give an extensive empirical comparison on the performance of various non-augmented base embedding with, and without IDF weighting. Our main results are that by training deep residual neural models specifically for retrieval purposes can yield significant gains when it is used to augment existing embeddings. We also establish that deeper models are superior to this task. The best base baseline embeddings augmented by our learned neural approach improves the top-1 recall of the system by 14% in terms of the question side, and by 8% in terms of the paragraph side.
 Summary: This paper proposes to reformulate the QA task in SQUAD as a retrieval task, i.e., using question as query and paragraphs as candidate results to be ranked. Authors makes some modifications to elmo model to create better word embedding for the ranking task. Authors have mentioned and are aware of open domain QA methodologies (e.g., DrQA). Pros: - The general idea is interesting, to reformulate any QA task as a ranking task Cons: - The methodology and task are not clear. Authors have reformulated QA in SQUAD as as ranking and never compared the results of the proposed model with other QA systems. If authors want to solve a pure ranking problem why they do not compare their methods with other ranking methods/datasets. - The novelty: The novelty is not significant. Although modifications to ELMO are interesting. - Results: Why authors have not compared their work with DrQA?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Text Embeddings for Retrieval from a Large Knowledge Base
Text embedding representing natural language documents in a semantic vector space can be used for document retrieval using nearest neighbor lookup. In order to study the feasibility of neural models specialized for retrieval in a semantically meaningful way, we suggest the use of the Stanford Question Answering Dataset (SQuAD) in an open-domain question answering context, where the first task is to find paragraphs useful for answering a given question. First, we compare the quality of various text-embedding methods on the performance of retrieval and give an extensive empirical comparison on the performance of various non-augmented base embedding with, and without IDF weighting. Our main results are that by training deep residual neural models specifically for retrieval purposes can yield significant gains when it is used to augment existing embeddings. We also establish that deeper models are superior to this task. The best base baseline embeddings augmented by our learned neural approach improves the top-1 recall of the system by 14% in terms of the question side, and by 8% in terms of the paragraph side.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 Summary: This paper proposes to reformulate the QA task in SQUAD as a retrieval task, i.e., using question as query and paragraphs as candidate results to be ranked. Authors makes some modifications to elmo model to create better word embedding for the ranking task. Authors have mentioned and are aware of open domain QA methodologies (e.g., DrQA). Pros: - The general idea is interesting, to reformulate any QA task as a ranking task Cons: - The methodology and task are not clear. Authors have reformulated QA in SQUAD as as ranking and never compared the results of the proposed model with other QA systems. If authors want to solve a pure ranking problem why they do not compare their methods with other ranking methods/datasets. - The novelty: The novelty is not significant. Although modifications to ELMO are interesting. - Results: Why authors have not compared their work with DrQA?
assistant
 Summary: This paper proposes to reformulate the QA task in SQUAD as a retrieval task, i.e., using question as query and paragraphs as candidate results to be ranked. Authors makes some modifications to elmo model to create better word embedding for the ranking task. Authors have mentioned and are aware of open domain QA methodologies (e.g., DrQA). Pros: - The general idea is interesting, to reformulate any QA task as a ranking task Cons: - The methodology and task are not clear. Authors have reformulated QA in SQUAD as as ranking and never compared the results of the proposed model with other QA systems. If authors want to solve a pure ranking problem why they do not compare their methods with other ranking methods/datasets. - The novelty: The novelty is not significant. Although modifications to ELMO are interesting. - Results: Why authors have not compared their work with DrQA?