# An End-to-End Network for Emotion-Cause Pair Extraction

arXiv: https://arxiv.org/abs/2103.01544 · HTML: https://mlsanity.com/paper/2103.01544/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 33 references that are in the map.
- First posted on arXiv 2 Mar 2021.

## Builds on (most cited first, 11 of 11)

- [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)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018)
- [Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts](https://mlsanity.com/paper/1906.01267/) (2019)
- [Affect-LM: A Neural Language Model for Customizable Affective Text Generation](https://mlsanity.com/paper/1704.06851/) (2017)
- [Affect-Driven Dialog Generation](https://mlsanity.com/paper/1904.02793/) (2019)
- [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)
- [A Question Answering Approach to Emotion Cause Extraction](https://mlsanity.com/paper/1708.05482/) (2017)

## Built on by (most cited first, 15 of 15)

- [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)
- [Emotion Recognition based on Psychological Components in Guided Narratives for Emotion Regulation](https://mlsanity.com/paper/2305.10446/) (2023)
- [Enhancing Large Language Model with Decomposed Reasoning for Emotion Cause Pair Extraction](https://mlsanity.com/paper/2401.17716/) (2024)
- [Multi-Granularity Semantic Aware Graph Model for Reducing Position Bias in Emotion-Cause Pair Extraction](https://mlsanity.com/paper/2205.02132/) (2022)
- [Multi-Task Learning Framework for Extracting Emotion Cause Span and Entailment in Conversations](https://mlsanity.com/paper/2211.03742/) (2022)
- [JMI at SemEval 2024 Task 3: Two-step approach for multimodal ECAC using in-context learning with GPT and instruction-tuned Llama models](https://mlsanity.com/paper/2403.04798/) (2024)
- [Humor@IITK at SemEval-2021 Task 7: Large Language Models for Quantifying Humor and Offensiveness](https://mlsanity.com/paper/2104.00933/) (2021)
- [Pair-Based Joint Encoding with Relational Graph Convolutional Networks for Emotion-Cause Pair Extraction](https://mlsanity.com/paper/2212.01844/) (2022)
- [Dynamic Causal Disentanglement Model for Dialogue Emotion Detection](https://mlsanity.com/paper/2309.06928/) (2023)
- [Emotion-Cause Pair Extraction in Customer Reviews](https://mlsanity.com/paper/2112.03984/) (2021)
- [A Multi-turn Machine Reading Comprehension Framework with Rethink Mechanism for Emotion-Cause Pair Extraction](https://mlsanity.com/paper/2209.07972/) (2022)
- [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)
- [Why We Feel What We Feel: Joint Detection of Emotions and Their Opinion Triggers in E-commerce](https://mlsanity.com/paper/2507.04708/) (2025)
- [MEKiT: Multi-source Heterogeneous Knowledge Injection Method via Instruction Tuning for Emotion-Cause Pair Extraction](https://mlsanity.com/paper/2507.14887/) (2025)

## Abstract (arXiv)

The task of Emotion-Cause Pair Extraction (ECPE) aims to extract all potential clause-pairs of emotions and their corresponding causes in a document. Unlike the more well-studied task of Emotion Cause Extraction (ECE), ECPE does not require the emotion clauses to be provided as annotations. Previous works on ECPE have either followed a multi-stage approach where emotion extraction, cause extraction, and pairing are done independently or use complex architectures to resolve its limitations. In this paper, we propose an end-to-end model for the ECPE task. Due to the unavailability of an English language ECPE corpus, we adapt the NTCIR-13 ECE corpus and establish a baseline for the ECPE task on this dataset. On this dataset, the proposed method produces significant performance improvements (~6.5 increase in F1 score) over the multi-stage approach and achieves comparable performance to the state-of-the-art methods.