# Multi-Task Learning Framework for Extracting Emotion Cause Span and Entailment in Conversations

arXiv: https://arxiv.org/abs/2211.03742 · HTML: https://mlsanity.com/paper/2211.03742/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 45 references that are in the map.
- First posted on arXiv 7 Nov 2022.

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

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [A Multiscale Visualization of Attention in the Transformer Model](https://mlsanity.com/paper/1906.05714/) (2019)
- [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)
- [Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts](https://mlsanity.com/paper/1906.01267/) (2019)
- [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)
- [Affect-Driven Dialog Generation](https://mlsanity.com/paper/1904.02793/) (2019)
- [Hierarchical Pre-training for Sequence Labelling in Spoken Dialog](https://mlsanity.com/paper/2009.11152/) (2020)
- [COGMEN: COntextualized GNN based Multimodal Emotion recognitioN](https://mlsanity.com/paper/2205.02455/) (2022)
- [RTHN: A RNN-Transformer Hierarchical Network for Emotion Cause Extraction](https://mlsanity.com/paper/1906.01236/) (2019)
- [Adapting a Language Model for Controlled Affective Text Generation](https://mlsanity.com/paper/2011.04000/) (2020)
- [An End-to-End Network for Emotion-Cause Pair Extraction](https://mlsanity.com/paper/2103.01544/) (2021)
- [Multi-Sample Dropout for Accelerated Training and Better Generalization](https://mlsanity.com/paper/1905.09788/) (2019)
- [Fine-Grained Emotion Prediction by Modeling Emotion Definitions](https://mlsanity.com/paper/2107.12135/) (2021)
- [Shapes of Emotions: Multimodal Emotion Recognition in Conversations via Emotion Shifts](https://mlsanity.com/paper/2112.01938/) (2021)
- [An Experimental Study of The Effects of Position Bias on Emotion CauseExtraction](https://mlsanity.com/paper/2007.15066/) (2020)
- [End-to-end Emotion-Cause Pair Extraction via Learning to Link](https://mlsanity.com/paper/2002.10710/) (2020)
- [ECSP: A New Task for Emotion-Cause Span-Pair Extraction and Classification](https://mlsanity.com/paper/2003.03507/) (2020)
- [A Dual-Questioning Attention Network for Emotion-Cause Pair Extraction with Context Awareness](https://mlsanity.com/paper/2104.07221/) (2021)

## Built on by (most cited first, 3 of 3)

- [SemEval-2024 Task 3: Multimodal Emotion Cause Analysis in Conversations](https://mlsanity.com/paper/2405.13049/) (2024)
- [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)
- [Dynamic Causal Disentanglement Model for Dialogue Emotion Detection](https://mlsanity.com/paper/2309.06928/) (2023)

## Abstract (arXiv)

Predicting emotions expressed in text is a well-studied problem in the NLP community. Recently there has been active research in extracting the cause of an emotion expressed in text. Most of the previous work has done causal emotion entailment in documents. In this work, we propose neural models to extract emotion cause span and entailment in conversations. For learning such models, we use RECCON dataset, which is annotated with cause spans at the utterance level. In particular, we propose MuTEC, an end-to-end Multi-Task learning framework for extracting emotions, emotion cause, and entailment in conversations. This is in contrast to existing baseline models that use ground truth emotions to extract the cause. MuTEC performs better than the baselines for most of the data folds provided in the dataset.