It Couldn’t Help but Overhear: On the Limits of Modelling Meta-Communicative Grounding Acts with Supervised Learning

Brielen Madureira, David Schlangen


Abstract
Active participation in a conversation is key to building common ground, since understanding is jointly tailored by producers and recipients. Overhearers are deprived of the privilege of performing grounding acts and can only conjecture about intended meanings. Still, data generation and annotation, modelling, training and evaluation of NLP dialogue models place reliance on the overhearing paradigm. How much of the underlying grounding processes are thereby forfeited? As we show, there is evidence pointing to the impossibility of properly modelling human meta-communicative acts with data-driven learning models. In this paper, we discuss this issue and provide a preliminary analysis on the variability of human decisions for requesting clarification. Most importantly, we wish to bring this topic back to the community’s table, encouraging discussion on the consequences of having models designed to only “listen in’”.
Anthology ID:
2024.sigdial-1.13
Volume:
Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Month:
September
Year:
2024
Address:
Kyoto, Japan
Editors:
Tatsuya Kawahara, Vera Demberg, Stefan Ultes, Koji Inoue, Shikib Mehri, David Howcroft, Kazunori Komatani
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
149–158
Language:
URL:
https://aclanthology.org/2024.sigdial-1.13
DOI:
10.18653/v1/2024.sigdial-1.13
Bibkey:
Cite (ACL):
Brielen Madureira and David Schlangen. 2024. It Couldn’t Help but Overhear: On the Limits of Modelling Meta-Communicative Grounding Acts with Supervised Learning. In Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 149–158, Kyoto, Japan. Association for Computational Linguistics.
Cite (Informal):
It Couldn’t Help but Overhear: On the Limits of Modelling Meta-Communicative Grounding Acts with Supervised Learning (Madureira & Schlangen, SIGDIAL 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.sigdial-1.13.pdf