Field |
Value |
Language |
dc.contributor.author |
Shen, T |
|
dc.contributor.author |
Geng, X |
|
dc.contributor.author |
Long, G
https://orcid.org/0000-0003-3740-9515
|
|
dc.contributor.author |
Jiang, J |
|
dc.contributor.author |
Zhang, C
https://orcid.org/0000-0001-5715-7154
|
|
dc.contributor.author |
Jiang, D |
|
dc.date |
2020-07-11 |
|
dc.date.accessioned |
2021-04-29T10:16:14Z |
|
dc.date.available |
2021-04-29T10:16:14Z |
|
dc.date.issued |
2020-07 |
|
dc.identifier.citation |
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020, pp. 2227-2233 |
|
dc.identifier.isbn |
9780999241165 |
|
dc.identifier.uri |
http://hdl.handle.net/10453/148543
|
|
dc.description.abstract |
<jats:p>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 and deliver a new state-of-the-art performance.</jats:p> |
|
dc.language |
en |
|
dc.publisher |
International Joint Conferences on Artificial Intelligence Organization |
|
dc.relation |
MakeMagic PTY. LTD |
|
dc.relation.ispartof |
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence |
|
dc.relation.ispartof |
Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20} |
|
dc.relation.isbasedon |
10.24963/ijcai.2020/308 |
|
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 |
|
pubs.organisational-group |
/University of Technology Sydney |
|
pubs.organisational-group |
/University of Technology Sydney/DVC (International) |
|
pubs.organisational-group |
/University of Technology Sydney/Faculty of Engineering and Information Technology |
|
pubs.organisational-group |
/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute |
|
pubs.organisational-group |
/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute |
|
pubs.organisational-group |
/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science |
|
utslib.copyright.status |
open_access |
* |
dc.date.updated |
2021-04-29T10:16:13Z |
|
pubs.finish-date |
2020-07-17 |
|
pubs.publication-status |
Published |
|
pubs.start-date |
2020-07-11 |
|