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- Anthology ID:
- 2023.findings-emnlp.22
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 278–293
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2023.findings-emnlp.22/
- DOI:
- 10.18653/v1/2023.findings-emnlp.22
- Cite (ACL):
- Zhengxiang Shi, Procheta Sen, and Aldo Lipani. 2023. Lexical Entrainment for Conversational Systems. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 278–293, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Lexical Entrainment for Conversational Systems (Shi et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2023.findings-emnlp.22.pdf