# BiosERC: Integrating Biography Speakers Supported by LLMs for ERC Tasks

arXiv: https://arxiv.org/abs/2407.04279 · HTML: https://mlsanity.com/paper/2407.04279/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 34 references that are in the map.
- First posted on arXiv 5 Jul 2024.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [DialogueRNN: An Attentive RNN for Emotion Detection in Conversations](https://mlsanity.com/paper/1811.00405/) (2018)
- [DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation](https://mlsanity.com/paper/1908.11540/) (2019)
- [COSMIC: COmmonSense knowledge for eMotion Identification in Conversations](https://mlsanity.com/paper/2010.02795/) (2020)
- [Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks](https://mlsanity.com/paper/1708.04299/) (2017)
- [Directed Acyclic Graph Network for Conversational Emotion Recognition](https://mlsanity.com/paper/2105.12907/) (2021)
- [DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion Recognition](https://mlsanity.com/paper/2012.08695/) (2020)
- [Recognizing Emotion Cause in Conversations](https://mlsanity.com/paper/2012.11820/) (2020)
- [UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition](https://mlsanity.com/paper/2211.11256/) (2022)
- [EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa](https://mlsanity.com/paper/2108.12009/) (2021)
- [CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in Conversation](https://mlsanity.com/paper/2108.11626/) (2021)
- [Graph Based Network with Contextualized Representations of Turns in Dialogue](https://mlsanity.com/paper/2109.04008/) (2021)
- [S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation](https://mlsanity.com/paper/2112.12389/) (2021)
- [Speaker-Guided Encoder-Decoder Framework for Emotion Recognition in Conversation](https://mlsanity.com/paper/2206.03173/) (2022)

## Built on by (most cited first, 11 of 11)

- [Beyond Silent Letters: Amplifying LLMs in Emotion Recognition with Vocal Nuances](https://mlsanity.com/paper/2407.21315/) (2024)
- [Akan Cinematic Emotions (ACE): A Multimodal Multi-party Dataset for Emotion Recognition in Movie Dialogues](https://mlsanity.com/paper/2502.10973/) (2025)
- [GatedxLSTM: A Multimodal Affective Computing Approach for Emotion Recognition in Conversations](https://mlsanity.com/paper/2503.20919/) (2025)
- [Long-Short Distance Graph Neural Networks and Improved Curriculum Learning for Emotion Recognition in Conversation](https://mlsanity.com/paper/2507.15205/) (2025)
- [Do LLMs Feel? Teaching Emotion Recognition with Prompts, Retrieval, and Curriculum Learning](https://mlsanity.com/paper/2511.07061/) (2025)
- [SCoPE: Shift-Aware Speaker-Conditioned Priors for Emotion Recognition in Conversations](https://mlsanity.com/paper/2607.20445/) (2026)
- [In-Context Examples Matter: Improving Emotion Recognition in Conversation with Instruction Tuning](https://mlsanity.com/paper/2508.11889/) (2025)
- [JNLP at SemEval-2025 Task 11: Cross-Lingual Multi-Label Emotion Detection Using Generative Models](https://mlsanity.com/paper/2505.13244/) (2025)
- [Expert-Guided Multimodal Fusion for Unified Emotion and Sentiment Analysis](https://mlsanity.com/paper/2601.07565/) (2026)
- [To Fuse or to Drop? Dual-Path Learning for Resolving Modality Conflicts in Multimodal Emotion Recognition](https://mlsanity.com/paper/2605.04877/) (2026)
- [DiaRelay: Relaying Dialogue Context with a Constant-Size Memory for Emotion Recognition in Conversation](https://mlsanity.com/paper/2608.22745/) (2026)

## Abstract (arXiv)

In the Emotion Recognition in Conversation task, recent investigations have utilized attention mechanisms exploring relationships among utterances from intra- and inter-speakers for modeling emotional interaction between them. However, attributes such as speaker personality traits remain unexplored and present challenges in terms of their applicability to other tasks or compatibility with diverse model architectures. Therefore, this work introduces a novel framework named BiosERC, which investigates speaker characteristics in a conversation. By employing Large Language Models (LLMs), we extract the "biographical information" of the speaker within a conversation as supplementary knowledge injected into the model to classify emotional labels for each utterance. Our proposed method achieved state-of-the-art (SOTA) results on three famous benchmark datasets: IEMOCAP, MELD, and EmoryNLP, demonstrating the effectiveness and generalization of our model and showcasing its potential for adaptation to various conversation analysis tasks. Our source code is available at https://github.com/yingjie7/BiosERC.