@inproceedings{peng-etal-2026-emodap,
title = "{E}mo{D}ap: Dual-Agent Prompt Retrieval Framework for Zero-Shot Fine-Grained Emotion Recognition",
author = "Peng, Kun and
Cao, Cong and
Peng, Hao and
Yu, Xiaoyan and
Wang, Zihao and
Jiang, Lei and
Liu, Yu and
Liu, Yanbing and
Yu, Philip S.",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1167/",
pages = "26752--26769",
ISBN = "979-8-89176-459-0",
abstract = "Zero-shot fine-grained emotion recognition (FER) is pivotal for empathetic AI but remains constrained by two inherent limitations in retrieval-augmented in-context learning (ICL): 1) subtle emotional distinctions inject semantic noise that blurs decision boundaries, and 2) naive similarity-based retrieval often surfaces counterproductive or antonymous demonstrations. To address these, we propose EmoDap, a dual-agent prompt retrieval framework for zero-shot FER. EmoDap first constructs a high-quality demonstration pool via self-consistency pseudo-labeling and derives explicit emotion prototypes through contrastive learning. Subsequently, a demonstration retriever and an emotion predictor are jointly optimized via Group Relative Policy Optimization (GRPO): the retriever learns to select examples that maximize the predictor{'}s accuracy, while the predictor leverages prototype guidance to dynamically filter candidate emotions. Extensive experiments across four benchmarks demonstrate that EmoDap consistently outperforms state-of-the-art baselines, establishing a robust paradigm for zero-shot FER driven by open-source LLM backbones."
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<abstract>Zero-shot fine-grained emotion recognition (FER) is pivotal for empathetic AI but remains constrained by two inherent limitations in retrieval-augmented in-context learning (ICL): 1) subtle emotional distinctions inject semantic noise that blurs decision boundaries, and 2) naive similarity-based retrieval often surfaces counterproductive or antonymous demonstrations. To address these, we propose EmoDap, a dual-agent prompt retrieval framework for zero-shot FER. EmoDap first constructs a high-quality demonstration pool via self-consistency pseudo-labeling and derives explicit emotion prototypes through contrastive learning. Subsequently, a demonstration retriever and an emotion predictor are jointly optimized via Group Relative Policy Optimization (GRPO): the retriever learns to select examples that maximize the predictor’s accuracy, while the predictor leverages prototype guidance to dynamically filter candidate emotions. Extensive experiments across four benchmarks demonstrate that EmoDap consistently outperforms state-of-the-art baselines, establishing a robust paradigm for zero-shot FER driven by open-source LLM backbones.</abstract>
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%0 Conference Proceedings
%T EmoDap: Dual-Agent Prompt Retrieval Framework for Zero-Shot Fine-Grained Emotion Recognition
%A Peng, Kun
%A Cao, Cong
%A Peng, Hao
%A Yu, Xiaoyan
%A Wang, Zihao
%A Jiang, Lei
%A Liu, Yu
%A Liu, Yanbing
%A Yu, Philip S.
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F peng-etal-2026-emodap
%X Zero-shot fine-grained emotion recognition (FER) is pivotal for empathetic AI but remains constrained by two inherent limitations in retrieval-augmented in-context learning (ICL): 1) subtle emotional distinctions inject semantic noise that blurs decision boundaries, and 2) naive similarity-based retrieval often surfaces counterproductive or antonymous demonstrations. To address these, we propose EmoDap, a dual-agent prompt retrieval framework for zero-shot FER. EmoDap first constructs a high-quality demonstration pool via self-consistency pseudo-labeling and derives explicit emotion prototypes through contrastive learning. Subsequently, a demonstration retriever and an emotion predictor are jointly optimized via Group Relative Policy Optimization (GRPO): the retriever learns to select examples that maximize the predictor’s accuracy, while the predictor leverages prototype guidance to dynamically filter candidate emotions. Extensive experiments across four benchmarks demonstrate that EmoDap consistently outperforms state-of-the-art baselines, establishing a robust paradigm for zero-shot FER driven by open-source LLM backbones.
%U https://aclanthology.org/2026.emnlp-main.1167/
%P 26752-26769
Markdown (Informal)
[EmoDap: Dual-Agent Prompt Retrieval Framework for Zero-Shot Fine-Grained Emotion Recognition](https://aclanthology.org/2026.emnlp-main.1167/) (Peng et al., EMNLP 2026)
ACL
- Kun Peng, Cong Cao, Hao Peng, Xiaoyan Yu, Zihao Wang, Lei Jiang, Yu Liu, Yanbing Liu, and Philip S. Yu. 2026. EmoDap: Dual-Agent Prompt Retrieval Framework for Zero-Shot Fine-Grained Emotion Recognition. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 26752–26769, Budapest, Hungary. Association for Computational Linguistics.