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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.
|Title of host publication||Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)|
|Place of Publication||Melbourne, Australia|
|Publisher||Association for Computational Linguistics|
|Number of pages||11|
|Publication status||Published - Jul 2018|
|Event||56th Annual Meeting of the Association for Computational Linguistics - Melbourne Convention and Exhibition Centre, Melbourne, Australia|
Duration: 15 Jul 2018 → 20 Jul 2018
|Conference||56th Annual Meeting of the Association for Computational Linguistics|
|Abbreviated title||ACL 2018|
|Period||15/07/18 → 20/07/18|
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TransModal: Translating from Multiple Modalities into Text
1/09/16 → 31/08/22