TY - GEN
T1 - WorldTree V2
T2 - 12th International Conference on Language Resources and Evaluation, LREC 2020
AU - Xie, Zhengnan
AU - Thiem, Sebastian
AU - Martin, Jaycie
AU - Wainwright, Elizabeth
AU - Marmorstein, Steven
AU - Jansen, Peter
N1 - Publisher Copyright:
© European Language Resources Association (ELRA), licensed under CC-BY-NC
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Explanations
KW - Multi-hop inference
KW - Question answering
UR - http://www.scopus.com/inward/record.url?scp=85096602945&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85096602945&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85096602945
T3 - LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings
SP - 5456
EP - 5473
BT - LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings
A2 - Calzolari, Nicoletta
A2 - Bechet, Frederic
A2 - Blache, Philippe
A2 - Choukri, Khalid
A2 - Cieri, Christopher
A2 - Declerck, Thierry
A2 - Goggi, Sara
A2 - Isahara, Hitoshi
A2 - Maegaard, Bente
A2 - Mariani, Joseph
A2 - Mazo, Helene
A2 - Moreno, Asuncion
A2 - Odijk, Jan
A2 - Piperidis, Stelios
PB - European Language Resources Association (ELRA)
Y2 - 11 May 2020 through 16 May 2020
ER -