Open-domain Topic Identification of Out-of-domain Utterances using Wikipedia
Abstract: Users of spoken dialogue systems (SDS) expect high quality interactions across a wide range of diverse topics. However, the implementation of SDS capable of responding to every conceivable user utterance in an informative way is a challenging problem. Multi-domain SDS must necessarily identify and deal with out-of-domain (OOD) utterances to generate appropriate responses as users do not always know in advance what domains the SDS can handle. To address this problem, we extend the current state-of-the-art in multi-domain SDS by estimating the topic of OOD utterances using external knowledge representation from Wikipedia. Experimental results on real human-to-human dialogues showed that our approach does not degrade domain prediction performance when compared to the base model. But more significantly, our joint training achieves more accurate predictions of the nearest Wikipedia article by up to about 30% when compared to the benchmarks.
Authors: A. Augustin, A. Papangelis, M. Kotti, P. Vougiouklis, J. Hare, N. Braunschweiler
Date: 2020-03-03
Venue: Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS), Workshop on Human in the Loop Dialogue Systems (HLDS)
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