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
@article{arxiv.2011.12662,
title = {XTQA: Span-Level Explanations of the Textbook Question Answering},
author = {Jie Ma and Qi Chai and Jun Liu and Qingyu Yin and Pinghui Wang and Qinghua Zheng},
journal= {arXiv preprint arXiv:2011.12662},
year = {2023}
}