Effective Search of Logical Forms for Weakly Supervised Knowledge-Based
  Question Answering

Shen, T; Geng, X; Qin, T; Long, G; Jiang, J; Jiang, D

Effective Search of Logical Forms for Weakly Supervised Knowledge-Based Question Answering

Shen, T Geng, X Qin, T Long, G

Jiang, J

Jiang, D

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Publication Type:: Conference Proceeding
Citation:: IJCAI International Joint Conference on Artificial Intelligence, 2020, 2021-January, pp. 2227-2233
Issue Date:: 2020-03-05

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Field	Value	Language
dc.contributor.author	Shen, T
dc.contributor.author	Geng, X
dc.contributor.author	Qin, T
dc.contributor.author	Long, G https://orcid.org/0000-0003-3740-9515
dc.contributor.author	Jiang, J https://orcid.org/0000-0001-5301-7779
dc.contributor.author	Jiang, D
dc.date	2020-07-11
dc.date.accessioned	2021-03-03T06:17:52Z
dc.date.available	2021-03-03T06:17:52Z
dc.date.issued	2020-03-05
dc.identifier.citation	IJCAI International Joint Conference on Artificial Intelligence, 2020, 2021-January, pp. 2227-2233
dc.identifier.isbn	9780999241165
dc.identifier.issn	1045-0823
dc.identifier.uri	http://hdl.handle.net/10453/146726
dc.description.abstract	Many algorithms for Knowledge-Based Question Answering (KBQA) depend on semantic parsing, which translates a question to its logical form. When only weak supervision is provided, it is usually necessary to search valid logical forms for model training. However, a complex question typically involves a huge search space, which creates two main problems: 1) the solutions limited by computation time and memory usually reduce the success rate of the search, and 2) spurious logical forms in the search results degrade the quality of training data. These two problems lead to a poorly-trained semantic parsing model. In this work, we propose an effective search method for weakly supervised KBQA based on operator prediction for questions. With search space constrained by predicted operators, sufficient search paths can be explored, more valid logical forms can be derived, and operators possibly causing spurious logical forms can be avoided. As a result, a larger proportion of questions in a weakly supervised training set are equipped with logical forms, and fewer spurious logical forms are generated. Such high-quality training data directly contributes to a better semantic parsing model. Experimental results on one of the largest KBQA datasets (i.e., CSQA) verify the effectiveness of our approach: improving the precision from 67% to 72% and the recall from 67% to 72% in terms of the overall score.
dc.language	en
dc.relation.ispartof	IJCAI International Joint Conference on Artificial Intelligence
dc.relation.ispartof	International Joint Conference on Artificial Intelligence 2020
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Effective Search of Logical Forms for Weakly Supervised Knowledge-Based Question Answering
dc.type	Conference Proceeding
utslib.citation.volume	2021-January
utslib.location.activity	Yokohama, Japan
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2021-03-03T06:17:50Z
pubs.finish-date	2020-07-17
pubs.publication-status	Published
pubs.start-date	2020-07-11
pubs.volume	2021-January

Abstract:

Many algorithms for Knowledge-Based Question Answering (KBQA) depend on semantic parsing, which translates a question to its logical form. When only weak supervision is provided, it is usually necessary to search valid logical forms for model training. However, a complex question typically involves a huge search space, which creates two main problems: 1) the solutions limited by computation time and memory usually reduce the success rate of the search, and 2) spurious logical forms in the search results degrade the quality of training data. These two problems lead to a poorly-trained semantic parsing model. In this work, we propose an effective search method for weakly supervised KBQA based on operator prediction for questions. With search space constrained by predicted operators, sufficient search paths can be explored, more valid logical forms can be derived, and operators possibly causing spurious logical forms can be avoided. As a result, a larger proportion of questions in a weakly supervised training set are equipped with logical forms, and fewer spurious logical forms are generated. Such high-quality training data directly contributes to a better semantic parsing model. Experimental results on one of the largest KBQA datasets (i.e., CSQA) verify the effectiveness of our approach: improving the precision from 67% to 72% and the recall from 67% to 72% in terms of the overall score.

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http://hdl.handle.net/10453/146726