# Emotion Detection in Unfix-length-Context Conversation

arXiv: https://arxiv.org/abs/2302.06029 · HTML: https://mlsanity.com/paper/2302.06029/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 26 references that are in the map.
- First posted on arXiv 13 Feb 2023.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [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)
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019)
- [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 Recognition in Conversation: Research Challenges, Datasets, and Recent Advances](https://mlsanity.com/paper/1905.02947/) (2019)
- [Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks](https://mlsanity.com/paper/1708.04299/) (2017)
- [Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations](https://mlsanity.com/paper/1909.10681/) (2019)
- [Utterance-level Dialogue Understanding: An Empirical Study](https://mlsanity.com/paper/2009.13902/) (2020)
- [HieNet: Bidirectional Hierarchy Framework for Automated ICD Coding](https://mlsanity.com/paper/2212.04891/) (2022)
- [Automatically Extracting Information in Medical Dialogue: Expert System And Attention for Labelling](https://mlsanity.com/paper/2211.15544/) (2022)
- [CKG: Dynamic Representation Based on Context and Knowledge Graph](https://mlsanity.com/paper/2212.04909/) (2022)
- [Moto: Enhancing Embedding with Multiple Joint Factors for Chinese Text Classification](https://mlsanity.com/paper/2212.08105/) (2022)

## Abstract (arXiv)

We leverage different context windows when predicting the emotion of different utterances. New modules are included to realize variable-length context: 1) two speaker-aware units, which explicitly model inner- and inter-speaker dependencies to form distilled conversational context, and 2) a top-k normalization layer, which determines the most proper context windows from the conversational context to predict emotion. Experiments and ablation studies show that our approach outperforms several strong baselines on three public datasets.