Query-Reduction Networks for Question Answering
In this paper, we study the problem of question answering when reasoning over multiple facts is required. We propose Query-Reduction Network (QRN), a variant of Recurrent Neural Network (RNN) that effectively handles both short-term (local) and long-term (global) sequential dependencies to reason over multiple facts. QRN considers the context sentences as a sequence of state-changing triggers, and reduces the original query to a more informed query as it observes each trigger (context sentence) through time. Our experiments show that QRN produces the state-of-the-art results in bAbI QA and dialog tasks, and in a real goal-oriented dialog dataset. In addition, QRN formulation allows parallelization on RNN*s time axis, saving an order of magnitude in time complexity for training and inference.
This work introduces a new RNN model and shows how it can be used for QA in bAbI-like settings. I think the model is simple and interesting, and the results are strong. I would like the authors to also show results training on all tasks simultaneously, but other than that, I think it is an interesting model and it is nicely described and tested.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Query-Reduction Networks for Question Answering
In this paper, we study the problem of question answering when reasoning over multiple facts is required. We propose Query-Reduction Network (QRN), a variant of Recurrent Neural Network (RNN) that effectively handles both short-term (local) and long-term (global) sequential dependencies to reason over multiple facts. QRN considers the context sentences as a sequence of state-changing triggers, and reduces the original query to a more informed query as it observes each trigger (context sentence) through time. Our experiments show that QRN produces the state-of-the-art results in bAbI QA and dialog tasks, and in a real goal-oriented dialog dataset. In addition, QRN formulation allows parallelization on RNN*s time axis, saving an order of magnitude in time complexity for training and inference.
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 work introduces a new RNN model and shows how it can be used for QA in bAbI-like settings. I think the model is simple and interesting, and the results are strong. I would like the authors to also show results training on all tasks simultaneously, but other than that, I think it is an interesting model and it is nicely described and tested.
assistant
This work introduces a new RNN model and shows how it can be used for QA in bAbI-like settings. I think the model is simple and interesting, and the results are strong. I would like the authors to also show results training on all tasks simultaneously, but other than that, I think it is an interesting model and it is nicely described and tested.