# CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in Conversation

arXiv: https://arxiv.org/abs/2108.11626 · HTML: https://mlsanity.com/paper/2108.11626/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 47 references that are in the map.
- First posted on arXiv 26 Aug 2021.

## Builds on (most cited first, 24 of 24)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [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)
- [Tensor Fusion Network for Multimodal Sentiment Analysis](https://mlsanity.com/paper/1707.07250/) (2017)
- [DialogueRNN: An Attentive RNN for Emotion Detection in Conversations](https://mlsanity.com/paper/1811.00405/) (2018)
- [A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues](https://mlsanity.com/paper/1605.06069/) (2016)
- [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)
- [Towards Conversational Recommendation over Multi-Type Dialogs](https://mlsanity.com/paper/2005.03954/) (2020)
- [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)
- [DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations](https://mlsanity.com/paper/2106.01978/) (2021)
- [Modeling Multi-turn Conversation with Deep Utterance Aggregation](https://mlsanity.com/paper/1806.09102/) (2018)
- [Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection](https://mlsanity.com/paper/2106.01071/) (2021)
- [Real-Time Emotion Recognition via Attention Gated Hierarchical Memory Network](https://mlsanity.com/paper/1911.09075/) (2019)
- [DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification](https://mlsanity.com/paper/2008.06914/) (2020)
- [EmpTransfo: A Multi-head Transformer Architecture for Creating Empathetic Dialog Systems](https://mlsanity.com/paper/2003.02958/) (2020)
- [EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators](https://mlsanity.com/paper/1912.00819/) (2019)
- [TripleNet: Triple Attention Network for Multi-Turn Response Selection in Retrieval-based Chatbots](https://mlsanity.com/paper/1909.10666/) (2019)

## Built on by (most cited first, 30 of 35)

- [UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition](https://mlsanity.com/paper/2211.11256/) (2022)
- [Is ChatGPT Equipped with Emotional Dialogue Capabilities?](https://mlsanity.com/paper/2304.09582/) (2023)
- [Revisiting Multi-modal Emotion Learning with Broad State Space Models and Probability-guidance Fusion](https://mlsanity.com/paper/2404.17858/) (2024)
- [Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrum](https://mlsanity.com/paper/2404.17862/) (2024)
- [Supervised Prototypical Contrastive Learning for Emotion Recognition in Conversation](https://mlsanity.com/paper/2210.08713/) (2022)
- [Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions](https://mlsanity.com/paper/2403.01222/) (2024)
- [DER-GCN: Dialogue and Event Relation-Aware Graph Convolutional Neural Network for Multimodal Dialogue Emotion Recognition](https://mlsanity.com/paper/2312.10579/) (2023)
- [Efficient Long-distance Latent Relation-aware Graph Neural Network for Multi-modal Emotion Recognition in Conversations](https://mlsanity.com/paper/2407.00119/) (2024)
- [Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations](https://mlsanity.com/paper/2306.01505/) (2023)
- [UniSA: Unified Generative Framework for Sentiment Analysis](https://mlsanity.com/paper/2309.01339/) (2023)
- [Refashioning Emotion Recognition Modelling: The Advent of Generalised Large Models](https://mlsanity.com/paper/2308.11578/) (2023)
- [Revisiting Disentanglement and Fusion on Modality and Context in Conversational Multimodal Emotion Recognition](https://mlsanity.com/paper/2308.04502/) (2023)
- [TelME: Teacher-leading Multimodal Fusion Network for Emotion Recognition in Conversation](https://mlsanity.com/paper/2401.12987/) (2024)
- [BiosERC: Integrating Biography Speakers Supported by LLMs for ERC Tasks](https://mlsanity.com/paper/2407.04279/) (2024)
- [LARA: Linguistic-Adaptive Retrieval-Augmentation for Multi-Turn Intent Classification](https://mlsanity.com/paper/2403.16504/) (2024)
- [BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation](https://mlsanity.com/paper/2301.06745/) (2023)
- [UniMEEC: Towards Unified Multimodal Emotion Recognition and Emotion Cause](https://mlsanity.com/paper/2404.00403/) (2024)
- [Disentangled Variational Autoencoder for Emotion Recognition in Conversations](https://mlsanity.com/paper/2305.14071/) (2023)
- [A Change of Heart: Improving Speech Emotion Recognition through Speech-to-Text Modality Conversion](https://mlsanity.com/paper/2307.11584/) (2023)
- [The Emotion is Not One-hot Encoding: Learning with Grayscale Label for Emotion Recognition in Conversation](https://mlsanity.com/paper/2206.07359/) (2022)
- [Whose Emotion Matters? Speaking Activity Localisation without Prior Knowledge](https://mlsanity.com/paper/2211.15377/) (2022)
- [Emotion-Anchored Contrastive Learning Framework for Emotion Recognition in Conversation](https://mlsanity.com/paper/2403.20289/) (2024)
- [EmoTwiCS: A Corpus for Modelling Emotion Trajectories in Dutch Customer Service Dialogues on Twitter](https://mlsanity.com/paper/2310.06536/) (2023)
- [From Multilingual Complexity to Emotional Clarity: Leveraging Commonsense to Unveil Emotions in Code-Mixed Dialogues](https://mlsanity.com/paper/2310.13080/) (2023)
- [ESIHGNN: Event-State Interactions Infused Heterogeneous Graph Neural Network for Conversational Emotion Recognition](https://mlsanity.com/paper/2405.03960/) (2024)
- [Can Large Language Models Generate Effective Datasets for Emotion Recognition in Conversations?](https://mlsanity.com/paper/2508.05474/) (2025)
- [SCoPE: Shift-Aware Speaker-Conditioned Priors for Emotion Recognition in Conversations](https://mlsanity.com/paper/2607.20445/) (2026)
- [HIINT: Historical, Intra- and Inter- personal Dynamics Modeling with Cross-person Memory Transformer](https://mlsanity.com/paper/2305.12369/) (2023)
- [M-MELD: A Multilingual Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/2203.16799/) (2022)
- [SSLCL: An Efficient Model-Agnostic Supervised Contrastive Learning Framework for Emotion Recognition in Conversations](https://mlsanity.com/paper/2310.16676/) (2023)

## Abstract (arXiv)

As the use of interactive machines grow, the task of Emotion Recognition in Conversation (ERC) became more important. If the machine-generated sentences reflect emotion, more human-like sympathetic conversations are possible. Since emotion recognition in conversation is inaccurate if the previous utterances are not taken into account, many studies reflect the dialogue context to improve the performances. Many recent approaches show performance improvement by combining knowledge into modules learned from external structured data. However, structured data is difficult to access in non-English languages, making it difficult to extend to other languages. Therefore, we extract the pre-trained memory using the pre-trained language model as an extractor of external knowledge. We introduce CoMPM, which combines the speaker's pre-trained memory with the context model, and find that the pre-trained memory significantly improves the performance of the context model. CoMPM achieves the first or second performance on all data and is state-of-the-art among systems that do not leverage structured data. In addition, our method shows that it can be extended to other languages because structured knowledge is not required, unlike previous methods. Our code is available on github (https://github.com/rungjoo/CoMPM).