# XTQA: Span-Level Explanations of the Textbook Question Answering

Source: [XTQA: Span-Level Explanations of the Textbook Question Answering](<https://www.sota2.com/research/paper/68fa701a1e209131cea4901e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:24:25.429Z

Authors: Jie Ma, Qi Chai, Jun Liu, Qingyu Yin, Pinghui Wang, Qinghua Zheng

Published: 2020-11-25T11:44:12.000Z

## 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

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2011.12662>)

[PDF](<https://arxiv.org/pdf/2011.12662.pdf>)