Dynamic Integration of Background Knowledge in Neural NLU Systems
Common-sense or background knowledge is required to understand natural language, but in most neural natural language understanding (NLU) systems, the requisite background knowledge is indirectly acquired from static corpora. We develop a new reading architecture for the dynamic integration of explicit background knowledge in NLU models. A new task-agnostic reading module provides refined word representations to a task-specific NLU architecture by processing background knowledge in the form of free-text statements, together with the task-specific inputs. Strong performance on the tasks of document question answering (DQA) and recognizing textual entailment (RTE) demonstrate the effectiveness and flexibility of our approach. Analysis shows that our models learn to exploit knowledge selectively and in a semantically appropriate way.
 The quality of this paper is good. The presentation is clear but I find lack of description of a key topic. The proposed model is not very innovative but works fine for the DQA task. For the TE task, the proposed method does not perform better than the state-of-the-art systems. - As ESIM is one of the key components in the experiments, you should briefly introduce ESIM and explain how you incorporated with your vector representations into ESIM. - The reference of ESIM is not correct. - Figure 1 is hard to understand. What do you indicate with the box and arrow? Arrows seem to have some different meanings. - What corpus did you use to pre-train word vectors? - As the proposed method was successful for the QA task, you need to explain QA data sets and how the questions are solved. - I also expect performance and error analysis of the task results. - To claim "task-agnostic", you need to try to apply your method to other NLP tasks as well. - Page 3. .Sigma is not defined.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Dynamic Integration of Background Knowledge in Neural NLU Systems
Common-sense or background knowledge is required to understand natural language, but in most neural natural language understanding (NLU) systems, the requisite background knowledge is indirectly acquired from static corpora. We develop a new reading architecture for the dynamic integration of explicit background knowledge in NLU models. A new task-agnostic reading module provides refined word representations to a task-specific NLU architecture by processing background knowledge in the form of free-text statements, together with the task-specific inputs. Strong performance on the tasks of document question answering (DQA) and recognizing textual entailment (RTE) demonstrate the effectiveness and flexibility of our approach. Analysis shows that our models learn to exploit knowledge selectively and in a semantically appropriate way.
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>



 The quality of this paper is good. The presentation is clear but I find lack of description of a key topic. The proposed model is not very innovative but works fine for the DQA task. For the TE task, the proposed method does not perform better than the state-of-the-art systems. - As ESIM is one of the key components in the experiments, you should briefly introduce ESIM and explain how you incorporated with your vector representations into ESIM. - The reference of ESIM is not correct. - Figure 1 is hard to understand. What do you indicate with the box and arrow? Arrows seem to have some different meanings. - What corpus did you use to pre-train word vectors? - As the proposed method was successful for the QA task, you need to explain QA data sets and how the questions are solved. - I also expect performance and error analysis of the task results. - To claim "task-agnostic", you need to try to apply your method to other NLP tasks as well. - Page 3. .Sigma is not defined.
assistant
 The quality of this paper is good. The presentation is clear but I find lack of description of a key topic. The proposed model is not very innovative but works fine for the DQA task. For the TE task, the proposed method does not perform better than the state-of-the-art systems. - As ESIM is one of the key components in the experiments, you should briefly introduce ESIM and explain how you incorporated with your vector representations into ESIM. - The reference of ESIM is not correct. - Figure 1 is hard to understand. What do you indicate with the box and arrow? Arrows seem to have some different meanings. - What corpus did you use to pre-train word vectors? - As the proposed method was successful for the QA task, you need to explain QA data sets and how the questions are solved. - I also expect performance and error analysis of the task results. - To claim "task-agnostic", you need to try to apply your method to other NLP tasks as well. - Page 3. .Sigma is not defined.