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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Learning From Unannotated QA Pairs to Analogically Disambiguate and Answer Questions

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Maxwell Crouse

Northwestern University


Clifton McFate

Northwestern University


Kenneth Forbus

Northwestern University


DOI:

10.1609/aaai.v32i1.11329


Abstract:

Creating systems that can learn to answer natural language questions has been a longstanding challenge for artificial intelligence. Most prior approaches focused on producing a specialized language system for a particular domain and dataset, and they required training on a large corpus manually annotated with logical forms. This paper introduces an analogy-based approach that instead adapts an existing general purpose semantic parser to answer questions in a novel domain by jointly learning disambiguation heuristics and query construction templates from purely textual question-answer pairs. Our technique uses possible semantic interpretations of the natural language questions and answers to constrain a query-generation procedure, producing cases during training that are subsequently reused via analogical retrieval and composed to answer test questions. Bootstrapping an existing semantic parser in this way significantly reduces the number of training examples needed to accurately answer questions. We demonstrate the efficacy of our technique using the Geoquery corpus, on which it approaches state of the art performance using 10-fold cross validation, shows little decrease in performance with 2-folds, and achieves above 50% accuracy with as few as 10 examples.

Topics: AAAI

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HOW TO CITE:

Maxwell Crouse||Clifton McFate||Kenneth Forbus Learning From Unannotated QA Pairs to Analogically Disambiguate and Answer Questions Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Maxwell Crouse||Clifton McFate||Kenneth Forbus Learning From Unannotated QA Pairs to Analogically Disambiguate and Answer Questions AAAI 2018, .

Maxwell Crouse||Clifton McFate||Kenneth Forbus (2018). Learning From Unannotated QA Pairs to Analogically Disambiguate and Answer Questions. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Maxwell Crouse||Clifton McFate||Kenneth Forbus. Learning From Unannotated QA Pairs to Analogically Disambiguate and Answer Questions. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Maxwell Crouse||Clifton McFate||Kenneth Forbus. 2018. Learning From Unannotated QA Pairs to Analogically Disambiguate and Answer Questions. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Maxwell Crouse||Clifton McFate||Kenneth Forbus. (2018) "Learning From Unannotated QA Pairs to Analogically Disambiguate and Answer Questions", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Maxwell Crouse||Clifton McFate||Kenneth Forbus, "Learning From Unannotated QA Pairs to Analogically Disambiguate and Answer Questions", AAAI, p., 2018.

Maxwell Crouse||Clifton McFate||Kenneth Forbus. "Learning From Unannotated QA Pairs to Analogically Disambiguate and Answer Questions". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Maxwell Crouse||Clifton McFate||Kenneth Forbus. "Learning From Unannotated QA Pairs to Analogically Disambiguate and Answer Questions". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Maxwell Crouse||Clifton McFate||Kenneth Forbus. Learning From Unannotated QA Pairs to Analogically Disambiguate and Answer Questions. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
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