# Discovering Emotion and Reasoning its Flip in Multi-Party Conversations using Masked Memory Network and Transformer

arXiv: https://arxiv.org/abs/2103.12360 · HTML: https://mlsanity.com/paper/2103.12360/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 41 references that are in the map.
- First posted on arXiv 23 Mar 2021.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation](https://mlsanity.com/paper/1908.11540/) (2019)
- [Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations](https://mlsanity.com/paper/1909.10681/) (2019)
- [Interactive Attention Networks for Aspect-Level Sentiment Classification](https://mlsanity.com/paper/1709.00893/) (2017)
- [MoEL: Mixture of Empathetic Listeners](https://mlsanity.com/paper/1908.07687/) (2019)
- [Real-Time Emotion Recognition via Attention Gated Hierarchical Memory Network](https://mlsanity.com/paper/1911.09075/) (2019)
- [A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1610.08815/) (2016)
- [BiERU: Bidirectional Emotional Recurrent Unit for Conversational Sentiment Analysis](https://mlsanity.com/paper/2006.00492/) (2020)
- [Generating Empathetic Responses by Looking Ahead the User's Sentiment](https://mlsanity.com/paper/1906.08487/) (2019)
- [Conversational Transfer Learning for Emotion Recognition](https://mlsanity.com/paper/1910.04980/) (2019)

## Built on by (most cited first, 14 of 14)

- [Multi-modal Sarcasm Detection and Humor Classification in Code-mixed Conversations](https://mlsanity.com/paper/2105.09984/) (2021)
- [Speaker and Time-aware Joint Contextual Learning for Dialogue-act Classification in Counselling Conversations](https://mlsanity.com/paper/2111.06647/) (2021)
- [BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages](https://mlsanity.com/paper/2502.11926/) (2025)
- [SemEval 2024 -- Task 10: Emotion Discovery and Reasoning its Flip in Conversation (EDiReF)](https://mlsanity.com/paper/2402.18944/) (2024)
- [Emotion Flip Reasoning in Multiparty Conversations](https://mlsanity.com/paper/2306.13959/) (2023)
- [Transformer based neural networks for emotion recognition in conversations](https://mlsanity.com/paper/2405.11222/) (2024)
- [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)
- [Dialogue Agents 101: A Beginner's Guide to Critical Ingredients for Designing Effective Conversational Systems](https://mlsanity.com/paper/2307.07255/) (2023)
- [IITK at SemEval-2024 Task 10: Who is the speaker? Improving Emotion Recognition and Flip Reasoning in Conversations via Speaker Embeddings](https://mlsanity.com/paper/2404.04525/) (2024)
- [MasonTigers at SemEval-2024 Task 10: Emotion Discovery and Flip Reasoning in Conversation with Ensemble of Transformers and Prompting](https://mlsanity.com/paper/2407.00581/) (2024)
- [MTP: A Dataset for Multi-Modal Turning Points in Casual Conversations](https://mlsanity.com/paper/2409.14801/) (2024)
- [EmoBench-UA: A Benchmark Dataset for Emotion Detection in Ukrainian](https://mlsanity.com/paper/2505.23297/) (2025)
- [Multi-refined Feature Enhanced Sentiment Analysis Using Contextual Instruction](https://mlsanity.com/paper/2511.00537/) (2025)

## Abstract (arXiv)

Efficient discovery of a speaker's emotional states in a multi-party conversation is significant to design human-like conversational agents. During a conversation, the cognitive state of a speaker often alters due to certain past utterances, which may lead to a flip in their emotional state. Therefore, discovering the reasons (triggers) behind the speaker's emotion-flip during a conversation is essential to explain the emotion labels of individual utterances. In this paper, along with addressing the task of emotion recognition in conversations (ERC), we introduce a novel task - Emotion-Flip Reasoning (EFR), that aims to identify past utterances which have triggered one's emotional state to flip at a certain time. We propose a masked memory network to address the former and a Transformer-based network for the latter task. To this end, we consider MELD, a benchmark emotion recognition dataset in multi-party conversations for the task of ERC, and augment it with new ground-truth labels for EFR. An extensive comparison with five state-of-the-art models suggests improved performances of our models for both tasks. We further present anecdotal evidence and both qualitative and quantitative error analyses to support the superiority of our models compared to the baselines.