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.
 This paper proposed a retrieval model based on the residual network and evaluated the use of ELMo word embedding with/without IDF weight. The results showed that there are significant gain when adding the residual network on top of the word embedding. Pros: * This work set a strong baseline for the retrieving target paragraph for question answering on the SQuAD dataset. * The experiments were sounds and leverage interesting points -- the use of word embedding itself as the feature representation didn*t have as much impact to retrieval performance as the distance function. * The studied problem -- retrieval for answering question rather than getting the most relevant document worth more attention. Cons: * The motivation of using the word embedding and contextual word embedding over the TF-IDF feature wasn*t clear. Results on using simple feature like TF-IDF maybe useful to give readers better judgement of the use of word embedding. * The choice of dataset, SQuAD over more retrieval based QA like TrivialQA also wasn*t strongly motivated. Also, it would be nice to see how the QA result would be improve with better retrieval model. * Another use of TF-IDF/IDF and embedding is to use TF-IDF/IDF to identify the related document and then use word embedding to resolve semantic ambiguity. Do you have theoretical/empirical reason why this shouldn’t be considered? Comment on writing: - In Section 3.1: the dimension of the tensor should reflect the meaning (vocab size, embedding size or the number of documents) rather than numbers. - In Section 3.1: since the weighting for each document is not shared, it would be clearer to just use M and W for each document instead of M’, W* - In Section 3.1: Evaluation metrics, e.g., recall@k, ROC, AUC; technical details, for example, tensor dimension, optimizer hyperparameters should be moved to the experiment section
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>



 This paper proposed a retrieval model based on the residual network and evaluated the use of ELMo word embedding with/without IDF weight. The results showed that there are significant gain when adding the residual network on top of the word embedding. Pros: * This work set a strong baseline for the retrieving target paragraph for question answering on the SQuAD dataset. * The experiments were sounds and leverage interesting points -- the use of word embedding itself as the feature representation didn*t have as much impact to retrieval performance as the distance function. * The studied problem -- retrieval for answering question rather than getting the most relevant document worth more attention. Cons: * The motivation of using the word embedding and contextual word embedding over the TF-IDF feature wasn*t clear. Results on using simple feature like TF-IDF maybe useful to give readers better judgement of the use of word embedding. * The choice of dataset, SQuAD over more retrieval based QA like TrivialQA also wasn*t strongly motivated. Also, it would be nice to see how the QA result would be improve with better retrieval model. * Another use of TF-IDF/IDF and embedding is to use TF-IDF/IDF to identify the related document and then use word embedding to resolve semantic ambiguity. Do you have theoretical/empirical reason why this shouldn’t be considered? Comment on writing: - In Section 3.1: the dimension of the tensor should reflect the meaning (vocab size, embedding size or the number of documents) rather than numbers. - In Section 3.1: since the weighting for each document is not shared, it would be clearer to just use M and W for each document instead of M’, W* - In Section 3.1: Evaluation metrics, e.g., recall@k, ROC, AUC; technical details, for example, tensor dimension, optimizer hyperparameters should be moved to the experiment section
assistant
 This paper proposed a retrieval model based on the residual network and evaluated the use of ELMo word embedding with/without IDF weight. The results showed that there are significant gain when adding the residual network on top of the word embedding. Pros: * This work set a strong baseline for the retrieving target paragraph for question answering on the SQuAD dataset. * The experiments were sounds and leverage interesting points -- the use of word embedding itself as the feature representation didn*t have as much impact to retrieval performance as the distance function. * The studied problem -- retrieval for answering question rather than getting the most relevant document worth more attention. Cons: * The motivation of using the word embedding and contextual word embedding over the TF-IDF feature wasn*t clear. Results on using simple feature like TF-IDF maybe useful to give readers better judgement of the use of word embedding. * The choice of dataset, SQuAD over more retrieval based QA like TrivialQA also wasn*t strongly motivated. Also, it would be nice to see how the QA result would be improve with better retrieval model. * Another use of TF-IDF/IDF and embedding is to use TF-IDF/IDF to identify the related document and then use word embedding to resolve semantic ambiguity. Do you have theoretical/empirical reason why this shouldn’t be considered? Comment on writing: - In Section 3.1: the dimension of the tensor should reflect the meaning (vocab size, embedding size or the number of documents) rather than numbers. - In Section 3.1: since the weighting for each document is not shared, it would be clearer to just use M and W for each document instead of M’, W* - In Section 3.1: Evaluation metrics, e.g., recall@k, ROC, AUC; technical details, for example, tensor dimension, optimizer hyperparameters should be moved to the experiment section