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


Abstract
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.
Anthology ID:
2020.lrec-1.671
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
5456–5473
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.671
DOI:
Bibkey:
Cite (ACL):
Zhengnan Xie, Sebastian Thiem, Jaycie Martin, Elizabeth Wainwright, Steven Marmorstein, and Peter Jansen. 2020. WorldTree V2: A Corpus of Science-Domain Structured Explanations and Inference Patterns supporting Multi-Hop Inference. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 5456–5473, Marseille, France. European Language Resources Association.
Cite (Informal):
WorldTree V2: A Corpus of Science-Domain Structured Explanations and Inference Patterns supporting Multi-Hop Inference (Xie et al., LREC 2020)
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PDF:
https://aclanthology.org/2020.lrec-1.671.pdf