Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering
End-to-end neural models have made significant progress in question answering, however recent studies show that these models implicitly assume that the answer and evidence appear close together in a single document. In this work, we propose the Coarse-grain Fine-grain Coattention Network (CFC), a new question answering model that combines information from evidence across multiple documents. The CFC consists of a coarse-grain module that interprets documents with respect to the query then finds a relevant answer, and a fine-grain module which scores each candidate answer by comparing its occurrences across all of the documents with the query. We design these modules using hierarchies of coattention and self-attention, which learn to emphasize different parts of the input. On the Qangaroo WikiHop multi-evidence question answering task, the CFC obtains a new state-of-the-art result of 70.6% on the blind test set, outperforming the previous best by 3% accuracy despite not using pretrained contextual encoders.
 This paper proposes a method for multi-hop QA based on two separate modules, which are called coarse-grained and fine-grained modules. The coarse-grained module reads all of the supporting documents for QA, whereas the fine-grained one reads the local context surrounding each candidate entity*s mentions. Both modules are used to predict the score of a candidate entity being the answer, with the final result being the sum of the two scores. This is a fine paper and achieves a new state of the art on the Qangaroo multi-hop QA dataset. The paper is clearly written, presents the models intuitively, while not foregoing technical detail should that be interesting to a reader. I appreciated the ablation results, as well as the qualitative analyses. The overall idea of encoding different levels of context is an important one, and I am glad that this paper shows that this approach works for a complex QA task. There are two downsides to the paper. The first is that I am not sure it is really accurate to call the coarse-grained model as such, as it still seems to require passing every word in the supporting documents to an encoder. It seems to be more aimed at capturing global information from the supporting documents, rather than to make a quick, high-level pass at inference. The second weakness is that the coarse- and fine-grained modules barely interact at all, as their prediction scores are simply summed at the output layer. It is nice that even such a simple method of interaction already works so well, but I would have expected some exploration or comment on how more interactions could be enabled.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering
End-to-end neural models have made significant progress in question answering, however recent studies show that these models implicitly assume that the answer and evidence appear close together in a single document. In this work, we propose the Coarse-grain Fine-grain Coattention Network (CFC), a new question answering model that combines information from evidence across multiple documents. The CFC consists of a coarse-grain module that interprets documents with respect to the query then finds a relevant answer, and a fine-grain module which scores each candidate answer by comparing its occurrences across all of the documents with the query. We design these modules using hierarchies of coattention and self-attention, which learn to emphasize different parts of the input. On the Qangaroo WikiHop multi-evidence question answering task, the CFC obtains a new state-of-the-art result of 70.6% on the blind test set, outperforming the previous best by 3% accuracy despite not using pretrained contextual encoders.
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 proposes a method for multi-hop QA based on two separate modules, which are called coarse-grained and fine-grained modules. The coarse-grained module reads all of the supporting documents for QA, whereas the fine-grained one reads the local context surrounding each candidate entity*s mentions. Both modules are used to predict the score of a candidate entity being the answer, with the final result being the sum of the two scores. This is a fine paper and achieves a new state of the art on the Qangaroo multi-hop QA dataset. The paper is clearly written, presents the models intuitively, while not foregoing technical detail should that be interesting to a reader. I appreciated the ablation results, as well as the qualitative analyses. The overall idea of encoding different levels of context is an important one, and I am glad that this paper shows that this approach works for a complex QA task. There are two downsides to the paper. The first is that I am not sure it is really accurate to call the coarse-grained model as such, as it still seems to require passing every word in the supporting documents to an encoder. It seems to be more aimed at capturing global information from the supporting documents, rather than to make a quick, high-level pass at inference. The second weakness is that the coarse- and fine-grained modules barely interact at all, as their prediction scores are simply summed at the output layer. It is nice that even such a simple method of interaction already works so well, but I would have expected some exploration or comment on how more interactions could be enabled.
assistant
 This paper proposes a method for multi-hop QA based on two separate modules, which are called coarse-grained and fine-grained modules. The coarse-grained module reads all of the supporting documents for QA, whereas the fine-grained one reads the local context surrounding each candidate entity*s mentions. Both modules are used to predict the score of a candidate entity being the answer, with the final result being the sum of the two scores. This is a fine paper and achieves a new state of the art on the Qangaroo multi-hop QA dataset. The paper is clearly written, presents the models intuitively, while not foregoing technical detail should that be interesting to a reader. I appreciated the ablation results, as well as the qualitative analyses. The overall idea of encoding different levels of context is an important one, and I am glad that this paper shows that this approach works for a complex QA task. There are two downsides to the paper. The first is that I am not sure it is really accurate to call the coarse-grained model as such, as it still seems to require passing every word in the supporting documents to an encoder. It seems to be more aimed at capturing global information from the supporting documents, rather than to make a quick, high-level pass at inference. The second weakness is that the coarse- and fine-grained modules barely interact at all, as their prediction scores are simply summed at the output layer. It is nice that even such a simple method of interaction already works so well, but I would have expected some exploration or comment on how more interactions could be enabled.