Neural Module Networks for Reasoning over Text
Answering compositional questions that require multiple steps of reasoning against text is challenging, especially when they involve discrete, symbolic operations. Neural module networks (NMNs) learn to parse such questions as executable programs composed of learnable modules, performing well on synthetic visual QA domains. However, we find that it is challenging to learn these models for non-synthetic questions on open-domain text, where a model needs to deal with the diversity of natural language and perform a broader range of reasoning. We extend NMNs by: (a) introducing modules that reason over a paragraph of text, performing symbolic reasoning (such as arithmetic, sorting, counting) over numbers and dates in a probabilistic and differentiable manner; and (b) proposing an unsupervised auxiliary loss to help extract arguments associated with the events in text. Additionally, we show that a limited amount of heuristically-obtained question program and intermediate module output supervision provides sufficient inductive bias for accurate learning. Our proposed model significantly outperforms state-of-the-art models on a subset of the DROP dataset that poses a variety of reasoning challenges that are covered by our modules.
This paper proposes a model and a training framework for question answering which requires compositional reasoning over the input text, by building executable neural modules and training based on additional auxiliary supervision signals. I really like this paper and the approach taken: tackling complex QA tasks is an important topic and current state-of-the-art methods rely heavily on lexical similarities as mentioned in the paper. I think learning differentiable programs is a good direction to address this space. However in IMHO, the paper as it stands is premature for publication, the primary reason being the lack of strong experimental evidence that where the strength of this approach is compared to other methods compared. To be specific, the results in Table 2 are very close between MTMSN and the BERT-based model proposed and it*s not clear if the difference is because (1) the model is generally better; (2) this is a subset of the dataset that this model performs better; (3) this is because of the additional supervision signals provided (e.g. the results of Fig 2a without the aux-sup is almost the same as MTMSN) and if we provided similar auxiliary supervision for other models they would equally do well; (4) due to lack of reporting variance and error-bars across runs we see a small increase which may not be significant; ... Again, the paper is very interesting, but I don*t think it*s clear and thorough to experimentally prove that the overall approach is working better.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Neural Module Networks for Reasoning over Text
Answering compositional questions that require multiple steps of reasoning against text is challenging, especially when they involve discrete, symbolic operations. Neural module networks (NMNs) learn to parse such questions as executable programs composed of learnable modules, performing well on synthetic visual QA domains. However, we find that it is challenging to learn these models for non-synthetic questions on open-domain text, where a model needs to deal with the diversity of natural language and perform a broader range of reasoning. We extend NMNs by: (a) introducing modules that reason over a paragraph of text, performing symbolic reasoning (such as arithmetic, sorting, counting) over numbers and dates in a probabilistic and differentiable manner; and (b) proposing an unsupervised auxiliary loss to help extract arguments associated with the events in text. Additionally, we show that a limited amount of heuristically-obtained question program and intermediate module output supervision provides sufficient inductive bias for accurate learning. Our proposed model significantly outperforms state-of-the-art models on a subset of the DROP dataset that poses a variety of reasoning challenges that are covered by our modules.
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 model and a training framework for question answering which requires compositional reasoning over the input text, by building executable neural modules and training based on additional auxiliary supervision signals. I really like this paper and the approach taken: tackling complex QA tasks is an important topic and current state-of-the-art methods rely heavily on lexical similarities as mentioned in the paper. I think learning differentiable programs is a good direction to address this space. However in IMHO, the paper as it stands is premature for publication, the primary reason being the lack of strong experimental evidence that where the strength of this approach is compared to other methods compared. To be specific, the results in Table 2 are very close between MTMSN and the BERT-based model proposed and it*s not clear if the difference is because (1) the model is generally better; (2) this is a subset of the dataset that this model performs better; (3) this is because of the additional supervision signals provided (e.g. the results of Fig 2a without the aux-sup is almost the same as MTMSN) and if we provided similar auxiliary supervision for other models they would equally do well; (4) due to lack of reporting variance and error-bars across runs we see a small increase which may not be significant; ... Again, the paper is very interesting, but I don*t think it*s clear and thorough to experimentally prove that the overall approach is working better.
assistant
This paper proposes a model and a training framework for question answering which requires compositional reasoning over the input text, by building executable neural modules and training based on additional auxiliary supervision signals. I really like this paper and the approach taken: tackling complex QA tasks is an important topic and current state-of-the-art methods rely heavily on lexical similarities as mentioned in the paper. I think learning differentiable programs is a good direction to address this space. However in IMHO, the paper as it stands is premature for publication, the primary reason being the lack of strong experimental evidence that where the strength of this approach is compared to other methods compared. To be specific, the results in Table 2 are very close between MTMSN and the BERT-based model proposed and it*s not clear if the difference is because (1) the model is generally better; (2) this is a subset of the dataset that this model performs better; (3) this is because of the additional supervision signals provided (e.g. the results of Fig 2a without the aux-sup is almost the same as MTMSN) and if we provided similar auxiliary supervision for other models they would equally do well; (4) due to lack of reporting variance and error-bars across runs we see a small increase which may not be significant; ... Again, the paper is very interesting, but I don*t think it*s clear and thorough to experimentally prove that the overall approach is working better.