@inproceedings{tang-etal-2023-rsvp,
    title = "{RSVP}: Customer Intent Detection via Agent Response Contrastive and Generative Pre-Training",
    author = "Tang, Yu-Chien  and
      Wang, Wei-Yao  and
      Yen, An-Zi  and
      Peng, Wen-Chih",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.findings-emnlp.698/",
    doi = "10.18653/v1/2023.findings-emnlp.698",
    pages = "10400--10412",
    abstract = "The dialogue systems in customer services have been developed with neural models to provide users with precise answers and round-the-clock support in task-oriented conversations by detecting customer intents based on their utterances. Existing intent detection approaches have highly relied on adaptively pre-training language models with large-scale datasets, yet the predominant cost of data collection may hinder their superiority. In addition, they neglect the information within the conversational responses of the agents, which have a lower collection cost, but are significant to customer intent as agents must tailor their replies based on the customers' intent. In this paper, we propose RSVP, a self-supervised framework dedicated to task-oriented dialogues, which utilizes agent responses for pre-training in a two-stage manner. Specifically, we introduce two pre-training tasks to incorporate the relations of utterance-response pairs: 1) Response Retrieval by selecting a correct response from a batch of candidates, and 2) Response Generation by mimicking agents to generate the response to a given utterance. Our benchmark results for two real-world customer service datasets show that RSVP significantly outperforms the state-of-the-art baselines by 4.95{\%} for accuracy, 3.4{\%} for MRR@3, and 2.75{\%} for MRR@5 on average. Extensive case studies are investigated to show the validity of incorporating agent responses into the pre-training stage."
}