# Opponent Modeling in Negotiation Dialogues by Related Data Adaptation

arXiv: https://arxiv.org/abs/2205.00344 · HTML: https://mlsanity.com/paper/2205.00344/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 41 references that are in the map.
- First posted on arXiv 30 Apr 2022.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [Pretrained Transformers for Text Ranking: BERT and Beyond](https://mlsanity.com/paper/2010.06467/) (2020)
- [Deal or No Deal? End-to-End Learning for Negotiation Dialogues](https://mlsanity.com/paper/1706.05125/) (2017)
- [Decoupling Strategy and Generation in Negotiation Dialogues](https://mlsanity.com/paper/1808.09637/) (2018)
- [Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems](https://mlsanity.com/paper/1709.08071/) (2017)
- [CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems](https://mlsanity.com/paper/2103.15721/) (2021)
- [A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification](https://mlsanity.com/paper/1910.04176/) (2019)
- [Improving Dialog Systems for Negotiation with Personality Modeling](https://mlsanity.com/paper/2010.09954/) (2020)
- [Augmenting Non-Collaborative Dialog Systems with Explicit Semantic and Strategic Dialog History](https://mlsanity.com/paper/1909.13425/) (2019)
- [Learning Goal-oriented Dialogue Policy with Opposite Agent Awareness](https://mlsanity.com/paper/2004.09731/) (2020)
- [Show, Price and Negotiate: A Negotiator with Online Value Look-Ahead](https://mlsanity.com/paper/1905.03721/) (2019)

## Built on by (most cited first, 13 of 13)

- [Let's Negotiate! A Survey of Negotiation Dialogue Systems](https://mlsanity.com/paper/2212.09072/) (2022)
- [Are LLMs Effective Negotiators? Systematic Evaluation of the Multifaceted Capabilities of LLMs in Negotiation Dialogues](https://mlsanity.com/paper/2402.13550/) (2024)
- [SocialDial: A Benchmark for Socially-Aware Dialogue Systems](https://mlsanity.com/paper/2304.12026/) (2023)
- [Social Influence Dialogue Systems: A Survey of Datasets and Models For Social Influence Tasks](https://mlsanity.com/paper/2210.05664/) (2022)
- [KODIS: A Multicultural Dispute Resolution Dialogue Corpus](https://mlsanity.com/paper/2504.12723/) (2025)
- [Deal, or no deal (or who knows)? Forecasting Uncertainty in Conversations using Large Language Models](https://mlsanity.com/paper/2402.03284/) (2024)
- [We Argue to Agree: Towards Personality-Driven Argumentation-Based Negotiation Dialogue Systems for Tourism](https://mlsanity.com/paper/2509.11118/) (2025)
- [ASTRA: A Negotiation Agent with Adaptive and Strategic Reasoning via Tool-integrated Action for Dynamic Offer Optimization](https://mlsanity.com/paper/2503.07129/) (2025)
- [Measuring Fine-Grained Negotiation Tactics of Humans and LLMs in Diplomacy](https://mlsanity.com/paper/2512.18292/) (2025)
- [Distilling Bayesian Belief States into Language Models for Auditable Negotiation](https://mlsanity.com/paper/2605.04507/) (2026)
- [Let's Negotiate! A Survey of Negotiation Dialogue Systems](https://mlsanity.com/paper/2402.01097/) (2024)
- [PRISMA: Preference-Reinforced Self-Training Approach for Interpretable Emotionally Intelligent Negotiation Dialogues](https://mlsanity.com/paper/2604.18354/) (2026)
- [Predicting Decisions of AI Agents from Limited Interaction through Text-Tabular Modeling](https://mlsanity.com/paper/2605.12411/) (2026)

## Abstract (arXiv)

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.