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arXiv · 2010.02586

Knowing What You Know: Calibrating Dialogue Belief State Distributions via Ensembles

Carel van Niekerk·Michael Heck·Christian Geishauser·Hsien-Chin Lin·Nurul Lubis·Marco Moresi·Milica Gašić
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Abstract

The ability to accurately track what happens during a conversation is essential for the performance of a dialogue system. Current state-of-the-art multi-domain dialogue state trackers achieve just over 55% accuracy on the current go-to benchmark, which means that in almost every second dialogue turn they place full confidence in an incorrect dialogue state. Belief trackers, on the other hand, maintain a distribution over possible dialogue states. However, they lack in performance compared to dialogue state trackers, and do not produce well calibrated distributions. In this work we present state-of-the-art performance in calibration for multi-domain dialogue belief trackers using a calibrated ensemble of models. Our resulting dialogue belief tracker also outperforms previous dialogue belief tracking models in terms of accuracy.

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Carel van Niekerk, Michael Heck, Christian Geishauser, Hsien-Chin Lin, Nurul Lubis, Marco Moresi, Milica Gašić. 2020-10-06. Knowing What You Know: Calibrating Dialogue Belief State Distributions via Ensembles. https://arxiv.org/abs/2010.02586

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