@inproceedings{chen-etal-2023-travel,
    title = "{TRAVEL}: Tag-Aware Conversational {FAQ} Retrieval via Reinforcement Learning",
    author = "Chen, Yue  and
      Jin, Dingnan  and
      Huang, Chen  and
      Liu, Jia  and
      Lei, Wenqiang",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2023.emnlp-main.234/",
    doi = "10.18653/v1/2023.emnlp-main.234",
    pages = "3861--3872",
    abstract = "Efficiently retrieving FAQ questions that match users' intent is essential for online customer service. Existing methods aim to fully utilize the dynamic conversation context to enhance the semantic association between the user query and FAQ questions. However, the conversation context contains noise, e.g., users may click questions they don`t like, leading to inaccurate semantics modeling. To tackle this, we introduce tags of FAQ questions, which can help us eliminate irrelevant information. We later integrate them into a reinforcement learning framework and minimize the negative impact of irrelevant information in the dynamic conversation context. We experimentally demonstrate our efficiency and effectiveness on conversational FAQ retrieval compared to other baselines."
}