# Towards Emotion-Aware Agents For Negotiation Dialogues

arXiv: https://arxiv.org/abs/2107.13165 · HTML: https://mlsanity.com/paper/2107.13165/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 50 references that are in the map.
- First posted on arXiv 28 Jul 2021.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [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)
- [CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems](https://mlsanity.com/paper/2103.15721/) (2021)
- [Improving Dialog Systems for Negotiation with Personality Modeling](https://mlsanity.com/paper/2010.09954/) (2020)
- [DialoGraph: Incorporating Interpretable Strategy-Graph Networks into Negotiation Dialogues](https://mlsanity.com/paper/2106.00920/) (2021)
- [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, 10 of 10)

- [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)
- [SENSE-7: Taxonomy and Dataset for Measuring User Perceptions of Empathy in Sustained Human-AI Conversations](https://mlsanity.com/paper/2509.16437/) (2025)
- [Affective Conversational Agents: Understanding Expectations and Personal Influences](https://mlsanity.com/paper/2310.12459/) (2023)
- [Measuring Fine-Grained Negotiation Tactics of Humans and LLMs in Diplomacy](https://mlsanity.com/paper/2512.18292/) (2025)
- [Expanding the Role of Affective Phenomena in Multimodal Interaction Research](https://mlsanity.com/paper/2305.10827/) (2023)
- [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)

## Abstract (arXiv)

Negotiation is a complex social interaction that encapsulates emotional encounters in human decision-making. Virtual agents that can negotiate with humans are useful in pedagogy and conversational AI. To advance the development of such agents, we explore the prediction of two important subjective goals in a negotiation - outcome satisfaction and partner perception. Specifically, we analyze the extent to which emotion attributes extracted from the negotiation help in the prediction, above and beyond the individual difference variables. We focus on a recent dataset in chat-based negotiations, grounded in a realistic camping scenario. We study three degrees of emotion dimensions - emoticons, lexical, and contextual by leveraging affective lexicons and a state-of-the-art deep learning architecture. Our insights will be helpful in designing adaptive negotiation agents that interact through realistic communication interfaces.