# Emotion-Cause Pair Extraction as Question Answering

arXiv: https://arxiv.org/abs/2301.01982 · HTML: https://mlsanity.com/paper/2301.01982/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 23 references that are in the map.
- First posted on arXiv 5 Jan 2023.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [UnifiedQA: Crossing Format Boundaries With a Single QA System](https://mlsanity.com/paper/2005.00700/) (2020)
- [A Unified MRC Framework for Named Entity Recognition](https://mlsanity.com/paper/1910.11476/) (2019)
- [Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts](https://mlsanity.com/paper/1906.01267/) (2019)
- [SpanNER: Named Entity Re-/Recognition as Span Prediction](https://mlsanity.com/paper/2106.00641/) (2021)
- [Paradigm Shift in Natural Language Processing](https://mlsanity.com/paper/2109.12575/) (2021)
- [Coarse-to-Fine Pre-training for Named Entity Recognition](https://mlsanity.com/paper/2010.08210/) (2020)
- [A Question Answering Approach to Emotion Cause Extraction](https://mlsanity.com/paper/1708.05482/) (2017)
- [Position Bias Mitigation: A Knowledge-Aware Graph Model for Emotion Cause Extraction](https://mlsanity.com/paper/2106.03518/) (2021)
- [A Span Extraction Approach for Information Extraction on Visually-Rich Documents](https://mlsanity.com/paper/2106.00978/) (2021)
- [Guaranteeing Reproducibility in Deep Learning Competitions](https://mlsanity.com/paper/2005.06041/) (2020)
- [ECSP: A New Task for Emotion-Cause Span-Pair Extraction and Classification](https://mlsanity.com/paper/2003.03507/) (2020)

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

- [Towards Safer Operations: An Expert-involved Dataset of High-Pressure Gas Incidents for Preventing Future Failures](https://mlsanity.com/paper/2310.12074/) (2023)
- [Recognizing Conditional Causal Relationships about Emotions and Their Corresponding Conditions](https://mlsanity.com/paper/2311.16579/) (2023)
- [AIMA at SemEval-2024 Task 3: Simple Yet Powerful Emotion Cause Pair Analysis](https://mlsanity.com/paper/2501.11170/) (2025)

## Abstract (arXiv)

The task of Emotion-Cause Pair Extraction (ECPE) aims to extract all potential emotion-cause pairs of a document without any annotation of emotion or cause clauses. Previous approaches on ECPE have tried to improve conventional two-step processing schemes by using complex architectures for modeling emotion-cause interaction. In this paper, we cast the ECPE task to the question answering (QA) problem and propose simple yet effective BERT-based solutions to tackle it. Given a document, our Guided-QA model first predicts the best emotion clause using a fixed question. Then the predicted emotion is used as a question to predict the most potential cause for the emotion. We evaluate our model on a standard ECPE corpus. The experimental results show that despite its simplicity, our Guided-QA achieves promising results and is easy to reproduce. The code of Guided-QA is also provided.