"How Robust r u?": Evaluating Task-Oriented Dialogue Systems on Spoken Conversations

• URL: http://arxiv.org/abs/2109.13489v1
• Date: Tue, 28 Sep 2021 04:51:04 GMT
• Title: "How Robust r u?": Evaluating Task-Oriented Dialogue Systems on Spoken Conversations
• Authors: Seokhwan Kim, Yang Liu, Di Jin, Alexandros Papangelis, Karthik Gopalakrishnan, Behnam Hedayatnia, Dilek Hakkani-Tur
• Abstract summary: This work presents a new benchmark on spoken task-oriented conversations. We study multi-domain dialogue state tracking and knowledge-grounded dialogue modeling. Our data set enables speech-based benchmarking of task-oriented dialogue systems.
• Score: 87.95711406978157
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most prior work in dialogue modeling has been on written conversations mostly because of existing data sets. However, written dialogues are not sufficient to fully capture the nature of spoken conversations as well as the potential speech recognition errors in practical spoken dialogue systems. This work presents a new benchmark on spoken task-oriented conversations, which is intended to study multi-domain dialogue state tracking and knowledge-grounded dialogue modeling. We report that the existing state-of-the-art models trained on written conversations are not performing well on our spoken data, as expected. Furthermore, we observe improvements in task performances when leveraging n-best speech recognition hypotheses such as by combining predictions based on individual hypotheses. Our data set enables speech-based benchmarking of task-oriented dialogue systems.

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