XTQA: Span-Level Explanations of the Textbook Question Answering

• URL: http://arxiv.org/abs/2011.12662v4
• Date: Mon, 24 Jul 2023 13:22:58 GMT
• Title: XTQA: Span-Level Explanations of the Textbook Question Answering
• Authors: Jie Ma, Qi Chai, Jun Liu, Qingyu Yin, Pinghui Wang, Qinghua Zheng
• Abstract summary: Textbook Question Answering (TQA) is a task that one should answer a diagram/non-diagram question given a large multi-modal context. We propose a novel architecture towards span-level eXplanations of the TQA based on our proposed coarse-to-fine grained algorithm. Experimental results show that XTQA significantly improves the state-of-the-art performance compared with baselines.
• Score: 32.67922842489546
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Textbook Question Answering (TQA) is a task that one should answer a diagram/non-diagram question given a large multi-modal context consisting of abundant essays and diagrams. We argue that the explainability of this task should place students as a key aspect to be considered. To address this issue, we devise a novel architecture towards span-level eXplanations of the TQA (XTQA) based on our proposed coarse-to-fine grained algorithm, which can provide not only the answers but also the span-level evidences to choose them for students. This algorithm first coarsely chooses top $M$ paragraphs relevant to questions using the TF-IDF method, and then chooses top $K$ evidence spans finely from all candidate spans within these paragraphs by computing the information gain of each span to questions. Experimental results shows that XTQA significantly improves the state-of-the-art performance compared with baselines. The source code is available at https://github.com/keep-smile-001/opentqa

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