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Best of Both Worlds: Making High Accuracy Non-incremental Transformer-based Disfluency Detection Incremental

Morteza Rohanian, Julian Hough

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Volume: 1 (Long Papers)

Swansea University Author: Julian Hough

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DOI (Published version): 10.18653/v1/2021.acl-long.286

Abstract

While Transformer-based text classifiers pre-trained on large volumes of text have yielded significant improvements on a wide range of computational linguistics tasks, their implementations have been unsuitable for live incremental processing thus far, operating only on the level of complete sentenc...

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Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing
ISBN: 978-1-954085-52-7
Published: Stroudsburg, PA, USA Association for Computational Linguistics 2021
URI: https://cronfa.swan.ac.uk/Record/cronfa64934
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spelling v2 64934 2023-11-07 Best of Both Worlds: Making High Accuracy Non-incremental Transformer-based Disfluency Detection Incremental 082d773ae261d2bbf49434dd2608ab40 Julian Hough Julian Hough true false 2023-11-07 SCS While Transformer-based text classifiers pre-trained on large volumes of text have yielded significant improvements on a wide range of computational linguistics tasks, their implementations have been unsuitable for live incremental processing thus far, operating only on the level of complete sentence inputs. We address the challenge of introducing methods for word-by-word left-to-right incremental processing to Transformers such as BERT, models without an intrinsic sense of linear order. We modify the training method and live decoding of non-incremental models to detect speech disfluencies with minimum latency and without pre-segmentation of dialogue acts. We experiment with several decoding methods to predict the rightward context of the word currently being processed using a GPT-2 language model and apply a BERT-based disfluency detector to sequences, including predicted words. We show our method of incrementalising Transformers maintains most of their high non-incremental performance while operating strictly incrementally. We also evaluate our models’ incremental performance to establish the trade-off between incremental performance and final performance, using different prediction strategies. We apply our system to incremental speech recognition results as they arrive into a live system and achieve state-of-the-art results in this setting. Conference Paper/Proceeding/Abstract Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 1 (Long Papers) Association for Computational Linguistics Stroudsburg, PA, USA 978-1-954085-52-7 1 8 2021 2021-08-01 10.18653/v1/2021.acl-long.286 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2024-03-12T14:12:11.4450515 2023-11-07T22:09:55.0021775 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Morteza Rohanian 1 Julian Hough 2
title Best of Both Worlds: Making High Accuracy Non-incremental Transformer-based Disfluency Detection Incremental
spellingShingle Best of Both Worlds: Making High Accuracy Non-incremental Transformer-based Disfluency Detection Incremental
Julian Hough
title_short Best of Both Worlds: Making High Accuracy Non-incremental Transformer-based Disfluency Detection Incremental
title_full Best of Both Worlds: Making High Accuracy Non-incremental Transformer-based Disfluency Detection Incremental
title_fullStr Best of Both Worlds: Making High Accuracy Non-incremental Transformer-based Disfluency Detection Incremental
title_full_unstemmed Best of Both Worlds: Making High Accuracy Non-incremental Transformer-based Disfluency Detection Incremental
title_sort Best of Both Worlds: Making High Accuracy Non-incremental Transformer-based Disfluency Detection Incremental
author_id_str_mv 082d773ae261d2bbf49434dd2608ab40
author_id_fullname_str_mv 082d773ae261d2bbf49434dd2608ab40_***_Julian Hough
author Julian Hough
author2 Morteza Rohanian
Julian Hough
format Conference Paper/Proceeding/Abstract
container_title Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing
container_volume 1 (Long Papers)
publishDate 2021
institution Swansea University
isbn 978-1-954085-52-7
doi_str_mv 10.18653/v1/2021.acl-long.286
publisher Association for Computational Linguistics
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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description While Transformer-based text classifiers pre-trained on large volumes of text have yielded significant improvements on a wide range of computational linguistics tasks, their implementations have been unsuitable for live incremental processing thus far, operating only on the level of complete sentence inputs. We address the challenge of introducing methods for word-by-word left-to-right incremental processing to Transformers such as BERT, models without an intrinsic sense of linear order. We modify the training method and live decoding of non-incremental models to detect speech disfluencies with minimum latency and without pre-segmentation of dialogue acts. We experiment with several decoding methods to predict the rightward context of the word currently being processed using a GPT-2 language model and apply a BERT-based disfluency detector to sequences, including predicted words. We show our method of incrementalising Transformers maintains most of their high non-incremental performance while operating strictly incrementally. We also evaluate our models’ incremental performance to establish the trade-off between incremental performance and final performance, using different prediction strategies. We apply our system to incremental speech recognition results as they arrive into a live system and achieve state-of-the-art results in this setting.
published_date 2021-08-01T14:12:07Z
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score 11.01628

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