Answering Elementary Science Questions by Constructing Coherent Scenes using Background Knowledge

By Y. Li, Peter Clark. 2015

12 citations recorded by the source corpus.

Much of what we understand from text is not explicitly stated. Rather, the reader uses his/her knowledge to fill in gaps and create a coherent, mental picture or “scene” depicting what text appears to convey. The scene constitutes an understanding of the text, and can be used to answer questions that go beyond the text. Our goal is to answer elementary science questions, where this requirement is pervasive; A question will often give a partial description of a scene and ask the student about implicit information. We show that by using a simple “knowledge graph” representation of the question, we can leverage several large-scale linguistic resources to provide missing background knowledge, somewhat alleviating the knowledge bottleneck in previous approaches. The coherence of the best resulting scene, built from a question/answer-candidate pair, reflects the confidence that the answer candidate is correct, and thus can be used to answer multiple choice questions. Our experiments show that this approach outperforms competitive algorithms on several datasets tested. The significance of this work is thus to show that a simple “knowledge graph” representation allows a version of “interpretation as scene construction” to be made viable.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

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Citations in the corpus

Improving Question Answering with External Knowledge (2019, background): “, 2018) or existing external general knowledge graphs (Li and Clark, 2015; Sachan et al., 2016; Wang et al., 2018a,c; Zhong et al., 2018; Musa et al., 2018) such as ConceptNet (Speer et al.”
Crowdsourcing Multiple Choice Science Questions (2017, methodology): “The method described by Li and Clark (2015) evaluates the coherence of a scene constructed from the question enriched with background KB information, while Sachan et al. (2016) train an entailment model that derives the correct answer from background knowledge aligned with a max-margin ranker.”
Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering (2020, background): “…knowledge, either in the form of nodes (Yang and Mitchell, 2017; Wang et al., 2019), triples (Weissenborn et al., 2017; Mihaylov and Frank, 2018), paths (Bauer et al., 2018; Kundu et al., 2019; Lin et al., 2019), or sub-graphs (Li and Clark, 2015), and encode them to augment textual understanding.”