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        <title>SuperDialseg: A Large-scale Dataset for Supervised Dialogue Segmentation</title>
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        <namePart type="given">Junfeng</namePart>
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        <namePart type="given">Chengzhang</namePart>
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    <name type="personal">
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    <abstract>Dialogue segmentation is a crucial task for dialogue systems allowing a better understanding of conversational texts. Despite recent progress in unsupervised dialogue segmentation methods, their performances are limited by the lack of explicit supervised signals for training. Furthermore, the precise definition of segmentation points in conversations still remains as a challenging problem, increasing the difficulty of collecting manual annotations. In this paper, we provide a feasible definition of dialogue segmentation points with the help of document-grounded dialogues and release a large-scale supervised dataset called SuperDialseg, containing 9,478 dialogues based on two prevalent document-grounded dialogue corpora, and also inherit their useful dialogue-related annotations. Moreover, we provide a benchmark including 18 models across five categories for the dialogue segmentation task with several proper evaluation metrics. Empirical studies show that supervised learning is extremely effective in in-domain datasets and models trained on SuperDialseg can achieve good generalization ability on out-of-domain data. Additionally, we also conducted human verification on the test set and the Kappa score confirmed the quality of our automatically constructed dataset. We believe our work is an important step forward in the field of dialogue segmentation.</abstract>
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%0 Conference Proceedings
%T SuperDialseg: A Large-scale Dataset for Supervised Dialogue Segmentation
%A Jiang, Junfeng
%A Dong, Chengzhang
%A Kurohashi, Sadao
%A Aizawa, Akiko
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F jiang-etal-2023-superdialseg
%X Dialogue segmentation is a crucial task for dialogue systems allowing a better understanding of conversational texts. Despite recent progress in unsupervised dialogue segmentation methods, their performances are limited by the lack of explicit supervised signals for training. Furthermore, the precise definition of segmentation points in conversations still remains as a challenging problem, increasing the difficulty of collecting manual annotations. In this paper, we provide a feasible definition of dialogue segmentation points with the help of document-grounded dialogues and release a large-scale supervised dataset called SuperDialseg, containing 9,478 dialogues based on two prevalent document-grounded dialogue corpora, and also inherit their useful dialogue-related annotations. Moreover, we provide a benchmark including 18 models across five categories for the dialogue segmentation task with several proper evaluation metrics. Empirical studies show that supervised learning is extremely effective in in-domain datasets and models trained on SuperDialseg can achieve good generalization ability on out-of-domain data. Additionally, we also conducted human verification on the test set and the Kappa score confirmed the quality of our automatically constructed dataset. We believe our work is an important step forward in the field of dialogue segmentation.
%R 10.18653/v1/2023.emnlp-main.249
%U https://aclanthology.org/2023.emnlp-main.249
%U https://doi.org/10.18653/v1/2023.emnlp-main.249
%P 4086-4101

Markdown (Informal)

[SuperDialseg: A Large-scale Dataset for Supervised Dialogue Segmentation](https://aclanthology.org/2023.emnlp-main.249) (Jiang et al., EMNLP 2023)

• SuperDialseg: A Large-scale Dataset for Supervised Dialogue Segmentation (Jiang et al., EMNLP 2023)

ACL

• Junfeng Jiang, Chengzhang Dong, Sadao Kurohashi, and Akiko Aizawa. 2023. SuperDialseg: A Large-scale Dataset for Supervised Dialogue Segmentation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 4086–4101, Singapore. Association for Computational Linguistics.