Stop: A Dataset for Spoken Task Oriented Semantic Parsing

Paden Tomasello, Akshat Shrivastava, Daniel Lazar, Po Chun Hsu, Duc Le, Adithya Sagar, Ali Elkahky, Jade Copet, Wei Ning Hsu, Yossi Adi, Robin Algayres, Tu Ahn Nguyen, Emmanuel Dupoux, Luke Zettlemoyer, Abdelrahman Mohamed

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

End-to-end spoken language understanding (SLU) predicts intent directly from audio using a single model. It promises to improve the performance of assistant systems by leveraging acoustic information lost in the intermediate textual representation and preventing cascading errors from Automatic Speech Recognition (ASR). Further, having one unified model has efficiency advantages when deploying assistant systems on-device. However, the limited number of public audio datasets with semantic parse labels hinders the research progress in this area. In this paper, we release the Spoken Task-Oriented semantic Parsing (STOP) dataset 1, the largest and most complex SLU dataset publicly available. Additionally, we define low-resource splits to establish a benchmark for improving SLU when limited labeled data is available. Furthermore, in addition to the human-recorded audio, we are releasing a TTS-generated versions to benchmark the performance for low-resource and domain adaptation of end-to-end SLU systems.

Original languageEnglish
Title of host publication2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages991-998
Number of pages8
ISBN (Electronic)9798350396904
DOIs
StatePublished - 2023
Externally publishedYes
Event2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Doha, Qatar
Duration: 9 Jan 202312 Jan 2023

Publication series

Name2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings

Conference

Conference2022 IEEE Spoken Language Technology Workshop, SLT 2022
Country/TerritoryQatar
CityDoha
Period9/01/2312/01/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • assistant
  • domain adaptation
  • spoken language understanding

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