# Multi-Task Learning with Auxiliary Speaker Identification for Conversational Emotion Recognition

arXiv: https://arxiv.org/abs/2003.01478 · HTML: https://mlsanity.com/paper/2003.01478/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2020.

## Built on by (most cited first, 12 of 12)

- [EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa](https://mlsanity.com/paper/2108.12009/) (2021)
- [M2FNet: Multi-modal Fusion Network for Emotion Recognition in Conversation](https://mlsanity.com/paper/2206.02187/) (2022)
- [Supervised Prototypical Contrastive Learning for Emotion Recognition in Conversation](https://mlsanity.com/paper/2210.08713/) (2022)
- [DialogueLLM: Context and Emotion Knowledge-Tuned Large Language Models for Emotion Recognition in Conversations](https://mlsanity.com/paper/2310.11374/) (2023)
- [Deep Emotion Recognition in Textual Conversations: A Survey](https://mlsanity.com/paper/2211.09172/) (2022)
- [InstructERC: Reforming Emotion Recognition in Conversation with Multi-task Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2309.11911/) (2023)
- [TempoFormer: A Transformer for Temporally-aware Representations in Change Detection](https://mlsanity.com/paper/2408.15689/) (2024)
- [Contextual Information and Commonsense Based Prompt for Emotion Recognition in Conversation](https://mlsanity.com/paper/2207.13254/) (2022)
- [Multitask Learning for Emotion and Personality Detection](https://mlsanity.com/paper/2101.02346/) (2021)
- [Samsung Research China-Beijing at SemEval-2024 Task 3: A multi-stage framework for Emotion-Cause Pair Extraction in Conversations](https://mlsanity.com/paper/2404.16905/) (2024)
- [Emotion Recognition in Conversation using Probabilistic Soft Logic](https://mlsanity.com/paper/2207.07238/) (2022)
- [OmniMER: Auxiliary-Enhanced LLM Adaptation for Indonesian Multimodal Emotion Recognition](https://mlsanity.com/paper/2512.19379/) (2025)

## Abstract (arXiv)

Conversational emotion recognition (CER) has attracted increasing interests in the natural language processing (NLP) community. Different from the vanilla emotion recognition, effective speaker-sensitive utterance representation is one major challenge for CER. In this paper, we exploit speaker identification (SI) as an auxiliary task to enhance the utterance representation in conversations. By this method, we can learn better speaker-aware contextual representations from the additional SI corpus. Experiments on two benchmark datasets demonstrate that the proposed architecture is highly effective for CER, obtaining new state-of-the-art results on two datasets.