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    <abstract>Large-scale pretrained transformer models have demonstrated state-of-the-art (SOTA) performance in a variety of NLP tasks. Nowadays, numerous pretrained models are available in different model flavors and different languages, and can be easily adapted to one’s downstream task. However, only a limited number of models are available for dialogue tasks, and in particular, goal-oriented dialogue tasks. In addition, the available pretrained models are trained on general domain language, creating a mismatch between the pretraining language and the downstream domain launguage. In this contribution, we present CS-BERT, a BERT model pretrained on millions of dialogues in the customer service domain. We evaluate CS-BERT on several downstream customer service dialogue tasks, and demonstrate that our in-domain pretraining is advantageous compared to other pretrained models in both zero-shot experiments as well as in finetuning experiments, especially in a low-resource data setting.</abstract>
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%0 Conference Proceedings
%T CS-BERT: a pretrained model for customer service dialogues
%A Wang, Peiyao
%A Fang, Joyce
%A Reinspach, Julia
%Y Papangelis, Alexandros
%Y Budzianowski, Paweł
%Y Liu, Bing
%Y Nouri, Elnaz
%Y Rastogi, Abhinav
%Y Chen, Yun-Nung
%S Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online
%F wang-etal-2021-cs
%X Large-scale pretrained transformer models have demonstrated state-of-the-art (SOTA) performance in a variety of NLP tasks. Nowadays, numerous pretrained models are available in different model flavors and different languages, and can be easily adapted to one’s downstream task. However, only a limited number of models are available for dialogue tasks, and in particular, goal-oriented dialogue tasks. In addition, the available pretrained models are trained on general domain language, creating a mismatch between the pretraining language and the downstream domain launguage. In this contribution, we present CS-BERT, a BERT model pretrained on millions of dialogues in the customer service domain. We evaluate CS-BERT on several downstream customer service dialogue tasks, and demonstrate that our in-domain pretraining is advantageous compared to other pretrained models in both zero-shot experiments as well as in finetuning experiments, especially in a low-resource data setting.
%R 10.18653/v1/2021.nlp4convai-1.13
%U https://aclanthology.org/2021.nlp4convai-1.13
%U https://doi.org/10.18653/v1/2021.nlp4convai-1.13
%P 130-142

Markdown (Informal)

[CS-BERT: a pretrained model for customer service dialogues](https://aclanthology.org/2021.nlp4convai-1.13) (Wang et al., NLP4ConvAI 2021)

• CS-BERT: a pretrained model for customer service dialogues (Wang et al., NLP4ConvAI 2021)

ACL

• Peiyao Wang, Joyce Fang, and Julia Reinspach. 2021. CS-BERT: a pretrained model for customer service dialogues. In Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI, pages 130–142, Online. Association for Computational Linguistics.