@inproceedings{shi-etal-2023-lexical,
    title = "Lexical Entrainment for Conversational Systems",
    author = "Shi, Zhengxiang  and
      Sen, Procheta  and
      Lipani, Aldo",
    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/ingest-emnlp/2023.findings-emnlp.22/",
    doi = "10.18653/v1/2023.findings-emnlp.22",
    pages = "278--293",
    abstract = "Conversational agents have become ubiquitous in assisting with daily tasks, and are expected to possess human-like features. One such feature is lexical entrainment (LE), a phenomenon in which speakers in human-human conversations tend to naturally and subconsciously align their lexical choices with those of their interlocutors, leading to more successful and engaging conversations. As an example, if a digital assistant replies ``Your appointment for Jinling Noodle Pub is at 7 pm'' to the question ``When is my reservation for Jinling Noodle Bar today?'', it may feel as though the assistant is trying to correct the speaker, whereas a response of ``Your reservation for Jinling Noodle Baris at 7 pm'' would likely be perceived as more positive. This highlights the importance of LE in establishing a shared terminology for maximum clarity and reducing ambiguity in conversations. However, we demonstrate in this work that current response generation models do not adequately address this crucial human-like phenomenon. To address this, we propose a new dataset, named MultiWOZ-ENTR, and a measure for LE for conversational systems. Additionally, we suggest a way to explicitly integrate LE into conversational systems with two new tasks, a LE extraction task and a LE generation task. We also present two baseline approaches for the LE extraction task, which aim to detect LE expressions from dialogue contexts"
}