Confidence Modeling for Neural Semantic Parsing

Li Dong, Chris Quirk, Maria Lapata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

In this work we focus on confidence modeling for neural semantic parsers which are built upon sequence-to-sequence models. We outline three major causes of uncertainty, and design various metrics to quantify these factors. These metrics are then used to estimate confidence scores that indicate whether model predictions are likely to be correct. Beyond confidence estimation, we identify which parts of the input contribute to uncertain predictions allowing users to interpret their model, and verify or refine its input. Experimental results show that our confidence model significantly outperforms a widely used method that relies on posterior probability, and improves the quality of interpretation compared to simply relying on attention scores.
Original languageEnglish
Title of host publicationProceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Place of PublicationMelbourne, Australia
PublisherAssociation for Computational Linguistics
Pages743-753
Number of pages11
Publication statusPublished - Jul 2018
Event56th Annual Meeting of the Association for Computational Linguistics - Melbourne Convention and Exhibition Centre, Melbourne, Australia
Duration: 15 Jul 201820 Jul 2018
http://acl2018.org/

Conference

Conference56th Annual Meeting of the Association for Computational Linguistics
Abbreviated titleACL 2018
Country/TerritoryAustralia
CityMelbourne
Period15/07/1820/07/18
Internet address

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