# Multi-Granularity Semantic Aware Graph Model for Reducing Position Bias in Emotion-Cause Pair Extraction

arXiv: https://arxiv.org/abs/2205.02132 · HTML: https://mlsanity.com/paper/2205.02132/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 4 May 2022.

## Builds on (most cited first, 7 of 7)

- [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)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts](https://mlsanity.com/paper/1906.01267/) (2019)
- [An End-to-End Network for Emotion-Cause Pair Extraction](https://mlsanity.com/paper/2103.01544/) (2021)
- [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, 6 of 6)

- [Knowledge-Bridged Causal Interaction Network for Causal Emotion Entailment](https://mlsanity.com/paper/2212.02995/) (2022)
- [Cause-Aware Empathetic Response Generation via Chain-of-Thought Fine-Tuning](https://mlsanity.com/paper/2408.11599/) (2024)
- [Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction](https://mlsanity.com/paper/2209.04112/) (2022)
- [Learning a General Clause-to-Clause Relationships for Enhancing Emotion-Cause Pair Extraction](https://mlsanity.com/paper/2208.13549/) (2022)
- [How to Enhance Causal Discrimination of Utterances: A Case on Affective Reasoning](https://mlsanity.com/paper/2305.02615/) (2023)
- [Co-evolving Graph Reasoning Network for Emotion-Cause Pair Extraction](https://mlsanity.com/paper/2306.04340/) (2023)

## Abstract (arXiv)

The Emotion-Cause Pair Extraction (ECPE) task aims to extract emotions and causes as pairs from documents. We observe that the relative distance distribution of emotions and causes is extremely imbalanced in the typical ECPE dataset. Existing methods have set a fixed size window to capture relations between neighboring clauses. However, they neglect the effective semantic connections between distant clauses, leading to poor generalization ability towards position-insensitive data. To alleviate the problem, we propose a novel Multi-Granularity Semantic Aware Graph model (MGSAG) to incorporate fine-grained and coarse-grained semantic features jointly, without regard to distance limitation. In particular, we first explore semantic dependencies between clauses and keywords extracted from the document that convey fine-grained semantic features, obtaining keywords enhanced clause representations. Besides, a clause graph is also established to model coarse-grained semantic relations between clauses. Experimental results indicate that MGSAG surpasses the existing state-of-the-art ECPE models. Especially, MGSAG outperforms other models significantly in the condition of position-insensitive data.