Elizabeth Wainwright


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WorldTree V2: A Corpus of Science-Domain Structured Explanations and Inference Patterns supporting Multi-Hop Inference
Zhengnan Xie | Sebastian Thiem | Jaycie Martin | Elizabeth Wainwright | Steven Marmorstein | Peter Jansen
Proceedings of the 12th Language Resources and Evaluation Conference

Explainable question answering for complex questions often requires combining large numbers of facts to answer a question while providing a human-readable explanation for the answer, a process known as multi-hop inference. Standardized science questions require combining an average of 6 facts, and as many as 16 facts, in order to answer and explain, but most existing datasets for multi-hop reasoning focus on combining only two facts, significantly limiting the ability of multi-hop inference algorithms to learn to generate large inferences. In this work we present the second iteration of the WorldTree project, a corpus of 5,114 standardized science exam questions paired with large detailed multi-fact explanations that combine core scientific knowledge and world knowledge. Each explanation is represented as a lexically-connected “explanation graph” that combines an average of 6 facts drawn from a semi-structured knowledge base of 9,216 facts across 66 tables. We use this explanation corpus to author a set of 344 high-level science domain inference patterns similar to semantic frames supporting multi-hop inference. Together, these resources provide training data and instrumentation for developing many-fact multi-hop inference models for question answering.


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WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-hop Inference
Peter Jansen | Elizabeth Wainwright | Steven Marmorstein | Clayton Morrison
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)