@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://aclanthology.org/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",
}
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<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</abstract>
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%0 Conference Proceedings
%T Lexical Entrainment for Conversational Systems
%A Shi, Zhengxiang
%A Sen, Procheta
%A Lipani, Aldo
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F shi-etal-2023-lexical
%X 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
%R 10.18653/v1/2023.findings-emnlp.22
%U https://aclanthology.org/2023.findings-emnlp.22
%U https://doi.org/10.18653/v1/2023.findings-emnlp.22
%P 278-293
Markdown (Informal)
[Lexical Entrainment for Conversational Systems](https://aclanthology.org/2023.findings-emnlp.22) (Shi et al., Findings 2023)
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.