TL;DR: We propose a neural question requirement inspection model called NeurQuRI that extracts a list of conditions from the question, each of which should be satisfied by the candidate answer generated by an MRC model.
Abstract: Real-world question answering systems often retrieve potentially relevant documents to a given question through a keyword search, followed by a machine reading comprehension (MRC) step to find the exact answer from them. In this process, it is essential to properly determine whether an answer to the question exists in a given document. This task often becomes complicated when the question involves multiple different conditions or requirements which are to be met in the answer. For example, in a question "What was the projection of sea level increases in the fourth assessment report?", the answer should properly satisfy several conditions, such as "increases" (but not decreases) and "fourth" (but not third). To address this, we propose a neural question requirement inspection model called NeurQuRI that extracts a list of conditions from the question, each of which should be satisfied by the candidate answer generated by an MRC model. To check whether each condition is met, we propose a novel, attention-based loss function. We evaluate our approach on SQuAD 2.0 dataset by integrating the proposed module with various MRC models, demonstrating the consistent performance improvements across a wide range of state-of-the-art methods.
Keywords: Question Answering, Machine Reading Comprehension, Answerability Prediction, Neural Checklist
Data: [MS MARCO](https://paperswithcode.com/dataset/ms-marco), [NewsQA](https://paperswithcode.com/dataset/newsqa), [SQuAD](https://paperswithcode.com/dataset/squad)
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