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arXiv · 2304.11073

OLISIA: a Cascade System for Spoken Dialogue State Tracking

Léo Jacqmin·Lucas Druart·Yannick Estève·Benoît Favre·Lina Maria Rojas-Barahona·Valentin Vielzeuf
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Abstract

Though Dialogue State Tracking (DST) is a core component of spoken dialogue systems, recent work on this task mostly deals with chat corpora, disregarding the discrepancies between spoken and written language.In this paper, we propose OLISIA, a cascade system which integrates an Automatic Speech Recognition (ASR) model and a DST model. We introduce several adaptations in the ASR and DST modules to improve integration and robustness to spoken conversations.With these adaptations, our system ranked first in DSTC11 Track 3, a benchmark to evaluate spoken DST. We conduct an in-depth analysis of the results and find that normalizing the ASR outputs and adapting the DST inputs through data augmentation, along with increasing the pre-trained models size all play an important role in reducing the performance discrepancy between written and spoken conversations.

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Léo Jacqmin, Lucas Druart, Yannick Estève, Benoît Favre, Lina Maria Rojas-Barahona, Valentin Vielzeuf. 2023-04-20. OLISIA: a Cascade System for Spoken Dialogue State Tracking. https://arxiv.org/abs/2304.11073

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