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- Khojah, Ranim, et al.
(författare)
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Evaluating N-best Calibration of Natural Language Understanding for Dialogue Systems
- 2022
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Ingår i: Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue, SIGDIAL 2022, 07-09 September 2022, Edinburgh, UK. - Stroudsburg, PA : Association for Computational Linguistics. - 9781955917667
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Konferensbidrag (refereegranskat)abstract
- A Natural Language Understanding (NLU) component can be used in a dialogue system to perform intent classification, returning an N-best list of hypotheses with corresponding confidence estimates. We perform an in-depth evaluation of 5 NLUs, focusing on confidence estimation. We measure and visualize calibration for the 10 best hypotheses on model level and rank level, and also measure classification performance. The results indicate a trade-off between calibration and performance. In particular, Rasa (with Sklearn classifier) had the best calibration but the lowest performance scores, while Watson Assistant had the best performance but a poor calibration.
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