# A Question Answering Approach to Emotion Cause Extraction

arXiv: https://arxiv.org/abs/1708.05482 · HTML: https://mlsanity.com/paper/1708.05482/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2017.

## Built on by (most cited first, 21 of 21)

- [Document Understanding Dataset and Evaluation (DUDE)](https://mlsanity.com/paper/2305.08455/) (2023)
- [Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes](https://mlsanity.com/paper/2109.08828/) (2021)
- [Multimodal Emotion-Cause Pair Extraction in Conversations](https://mlsanity.com/paper/2110.08020/) (2021)
- [Position Bias Mitigation: A Knowledge-Aware Graph Model for Emotion Cause Extraction](https://mlsanity.com/paper/2106.03518/) (2021)
- [An End-to-End Network for Emotion-Cause Pair Extraction](https://mlsanity.com/paper/2103.01544/) (2021)
- [Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction](https://mlsanity.com/paper/2209.04112/) (2022)
- [Empathetic Response Generation through Graph-based Multi-hop Reasoning on Emotional Causality](https://mlsanity.com/paper/2110.04614/) (2021)
- [TSAM: A Two-Stream Attention Model for Causal Emotion Entailment](https://mlsanity.com/paper/2203.00819/) (2022)
- [Emotion Stimulus Detection in German News Headlines](https://mlsanity.com/paper/2107.12920/) (2021)
- [Emotion Prediction Oriented method with Multiple Supervisions for Emotion-Cause Pair Extraction](https://mlsanity.com/paper/2302.12417/) (2023)
- [A Dual-Questioning Attention Network for Emotion-Cause Pair Extraction with Context Awareness](https://mlsanity.com/paper/2104.07221/) (2021)
- [Emotion-Cause Pair Extraction as Question Answering](https://mlsanity.com/paper/2301.01982/) (2023)
- [Co-evolving Graph Reasoning Network for Emotion-Cause Pair Extraction](https://mlsanity.com/paper/2306.04340/) (2023)
- [NoTeS-Bank: Benchmarking Neural Transcription and Search for Scientific Notes Understanding](https://mlsanity.com/paper/2504.09249/) (2025)
- [SGPT: Semantic Graphs based Pre-training for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2105.12305/) (2021)
- [On the Complementarity of Images and Text for the Expression of Emotions in Social Media](https://mlsanity.com/paper/2202.07427/) (2022)
- [Pair-Based Joint Encoding with Relational Graph Convolutional Networks for Emotion-Cause Pair Extraction](https://mlsanity.com/paper/2212.01844/) (2022)
- [A Multi-turn Machine Reading Comprehension Framework with Rethink Mechanism for Emotion-Cause Pair Extraction](https://mlsanity.com/paper/2209.07972/) (2022)
- [Recognizing Conditional Causal Relationships about Emotions and Their Corresponding Conditions](https://mlsanity.com/paper/2311.16579/) (2023)
- [Emotion-cause pair extraction method based on multi-granularity information and multi-module interaction](https://mlsanity.com/paper/2404.06812/) (2024)
- [Acquiring Common Chinese Emotional Events Using Large Language Model](https://mlsanity.com/paper/2511.04989/) (2025)

## Abstract (arXiv)

Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text. It is a much more difficult task compared to emotion classification. Inspired by recent advances in using deep memory networks for question answering (QA), we propose a new approach which considers emotion cause identification as a reading comprehension task in QA. Inspired by convolutional neural networks, we propose a new mechanism to store relevant context in different memory slots to model context information. Our proposed approach can extract both word level sequence features and lexical features. Performance evaluation shows that our method achieves the state-of-the-art performance on a recently released emotion cause dataset, outperforming a number of competitive baselines by at least 3.01% in F-measure.