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

doi:https://doi.org/10.1109/SLT54892.2023.10022703

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 proceeding › Conference contribution › peer-review

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 language	English
Title of host publication	2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	991-998
Number of pages	8
ISBN (Electronic)	9798350396904
DOIs	https://doi.org/10.1109/SLT54892.2023.10022703
State	Published - 2023
Externally published	Yes
Event	2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Doha, Qatar Duration: 9 Jan 2023 → 12 Jan 2023

Publication series

Name	2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings

Conference

Conference	2022 IEEE Spoken Language Technology Workshop, SLT 2022
Country/Territory	Qatar
City	Doha
Period	9/01/23 → 12/01/23

Keywords

assistant
domain adaptation
spoken language understanding

All Science Journal Classification (ASJC) codes

Computer Vision and Pattern Recognition
Hardware and Architecture
Media Technology
Instrumentation
Linguistics and Language

Access to Document

https://doi.org/10.1109/SLT54892.2023.10022703

Cite this

Tomasello, P., Shrivastava, A., Lazar, D., Hsu, P. C., Le, D., Sagar, A., Elkahky, A., Copet, J., Hsu, W. N., Adi, Y., Algayres, R., Nguyen, T. A., Dupoux, E., Zettlemoyer, L., & Mohamed, A. (2023). Stop: A Dataset for Spoken Task Oriented Semantic Parsing. In 2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings (pp. 991-998). (2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SLT54892.2023.10022703

@inproceedings{ed80ae614f5142e6946cfb6387aa34cd,

title = "Stop: A Dataset for Spoken Task Oriented Semantic Parsing",

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.",

keywords = "assistant, domain adaptation, spoken language understanding",

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

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2022 IEEE Spoken Language Technology Workshop, SLT 2022 ; Conference date: 09-01-2023 Through 12-01-2023",

year = "2023",

doi = "https://doi.org/10.1109/SLT54892.2023.10022703",

language = "الإنجليزيّة",

series = "2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "991--998",

booktitle = "2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings",

address = "الولايات المتّحدة",

}

Tomasello, P, Shrivastava, A, Lazar, D, Hsu, PC, Le, D, Sagar, A, Elkahky, A, Copet, J, Hsu, WN, Adi, Y, Algayres, R, Nguyen, TA, Dupoux, E, Zettlemoyer, L & Mohamed, A 2023, Stop: A Dataset for Spoken Task Oriented Semantic Parsing. in 2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings. 2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 991-998, 2022 IEEE Spoken Language Technology Workshop, SLT 2022, Doha, Qatar, 9/01/23. https://doi.org/10.1109/SLT54892.2023.10022703

Stop: A Dataset for Spoken Task Oriented Semantic Parsing. / Tomasello, Paden; Shrivastava, Akshat; Lazar, Daniel et al.
2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. p. 991-998 (2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Stop

T2 - 2022 IEEE Spoken Language Technology Workshop, SLT 2022

AU - Tomasello, Paden

AU - Shrivastava, Akshat

AU - Lazar, Daniel

AU - Hsu, Po Chun

AU - Le, Duc

AU - Sagar, Adithya

AU - Elkahky, Ali

AU - Copet, Jade

AU - Hsu, Wei Ning

AU - Adi, Yossi

AU - Algayres, Robin

AU - Nguyen, Tu Ahn

AU - Dupoux, Emmanuel

AU - Zettlemoyer, Luke

AU - Mohamed, Abdelrahman

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - assistant

KW - domain adaptation

KW - spoken language understanding

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U2 - https://doi.org/10.1109/SLT54892.2023.10022703

DO - https://doi.org/10.1109/SLT54892.2023.10022703

M3 - منشور من مؤتمر

T3 - 2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings

SP - 991

EP - 998

BT - 2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 9 January 2023 through 12 January 2023

ER -

Tomasello P, Shrivastava A, Lazar D, Hsu PC, Le D, Sagar A et al. Stop: A Dataset for Spoken Task Oriented Semantic Parsing. In 2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. p. 991-998. (2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings). doi: https://doi.org/10.1109/SLT54892.2023.10022703

Stop: A Dataset for Spoken Task Oriented Semantic Parsing

Abstract

Publication series

Conference

Keywords

All Science Journal Classification (ASJC) codes

Access to Document

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