Textbook Question Answering with Knowledge Graph Understanding and Unsupervised Open-set Text Comprehension
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In this work, we introduce a novel algorithm for solving the textbook question answering (TQA) task which describes more realistic QA problems compared to other recent tasks. We mainly focus on two related issues with analysis of TQA dataset. First, it requires to comprehend long lessons to extract knowledge. To tackle this issue of extracting knowledge features from long lessons, we establish knowledge graph from texts and incorporate graph convolutional network (GCN). Second, scientific terms are not spread over the chapters and data splits in TQA dataset. To overcome this so called `out-of-domain' issue, we add novel unsupervised text learning process without any annotations before learning QA problems. The experimental results show that our model significantly outperforms prior state-of-the-art methods. Moreover, ablation studies validate that both methods of incorporating GCN for extracting knowledge from long lessons and our newly proposed unsupervised learning process are meaningful to solve this problem.
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