Opponent Modeling in Negotiation Dialogues by Related Data Adaptation

Kushal Chawla, Gale Lucas, Jonathan May, Jonathan Gratch


Abstract
Opponent modeling is the task of inferring another party’s mental state within the context of social interactions. In a multi-issue negotiation, it involves inferring the relative importance that the opponent assigns to each issue under discussion, which is crucial for finding high-value deals. A practical model for this task needs to infer these priorities of the opponent on the fly based on partial dialogues as input, without needing additional annotations for training. In this work, we propose a ranker for identifying these priorities from negotiation dialogues. The model takes in a partial dialogue as input and predicts the priority order of the opponent. We further devise ways to adapt related data sources for this task to provide more explicit supervision for incorporating the opponent’s preferences and offers, as a proxy to relying on granular utterance-level annotations. We show the utility of our proposed approach through extensive experiments based on two dialogue datasets. We find that the proposed data adaptations lead to strong performance in zero-shot and few-shot scenarios. Moreover, they allow the model to perform better than baselines while accessing fewer utterances from the opponent. We release our code to support future work in this direction.
Anthology ID:
2022.findings-naacl.50
Volume:
Findings of the Association for Computational Linguistics: NAACL 2022
Month:
July
Year:
2022
Address:
Seattle, United States
Venues:
Findings | NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
661–674
Language:
URL:
https://aclanthology.org/2022.findings-naacl.50
DOI:
10.18653/v1/2022.findings-naacl.50
Bibkey:
Cite (ACL):
Kushal Chawla, Gale Lucas, Jonathan May, and Jonathan Gratch. 2022. Opponent Modeling in Negotiation Dialogues by Related Data Adaptation. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 661–674, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Opponent Modeling in Negotiation Dialogues by Related Data Adaptation (Chawla et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-naacl.50.pdf
Code
 kushalchawla/opponent-modeling
Data
CaSiNo