XTQA: Span-Level Explanations of the Textbook Question Answering
Jie Ma, Qi Chai, Jun Liu, Qingyu Yin, Pinghui Wang, Qinghua Zheng

TL;DR
This paper introduces XTQA, a novel model that provides span-level explanations for textbook question answering, enhancing interpretability and accuracy by identifying relevant evidence spans in multi-modal contexts.
Contribution
We propose a coarse-to-fine algorithm for span-level explanations in TQA, improving interpretability and state-of-the-art performance.
Findings
Significant performance improvement over baselines
Effective span-level evidence extraction
Enhanced explainability for TQA tasks
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 paragraphs relevant to questions using the TF-IDF method, and then chooses top 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…
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Taxonomy
TopicsTopic Modeling · Intelligent Tutoring Systems and Adaptive Learning · Multimodal Machine Learning Applications
