# Position Bias Mitigation: A Knowledge-Aware Graph Model for Emotion Cause Extraction

arXiv: https://arxiv.org/abs/2106.03518 · HTML: https://mlsanity.com/paper/2106.03518/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 35 references that are in the map.
- First posted on arXiv 7 Jun 2021.

## Builds on (most cited first, 17 of 17)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Universal Adversarial Triggers for Attacking and Analyzing NLP](https://mlsanity.com/paper/1908.07125/) (2019)
- [Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment](https://mlsanity.com/paper/1907.11932/) (2019)
- [DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning](https://mlsanity.com/paper/1707.06690/) (2017)
- [KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning](https://mlsanity.com/paper/1909.02151/) (2019)
- [Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts](https://mlsanity.com/paper/1906.01267/) (2019)
- [Recognizing Emotion Cause in Conversations](https://mlsanity.com/paper/2012.11820/) (2020)
- [Modeling Naive Psychology of Characters in Simple Commonsense Stories](https://mlsanity.com/paper/1805.06533/) (2018)
- [RTHN: A RNN-Transformer Hierarchical Network for Emotion Cause Extraction](https://mlsanity.com/paper/1906.01236/) (2019)
- [A Question Answering Approach to Emotion Cause Extraction](https://mlsanity.com/paper/1708.05482/) (2017)
- [From Independent Prediction to Re-ordered Prediction: Integrating Relative Position and Global Label Information to Emotion Cause Identification](https://mlsanity.com/paper/1906.01230/) (2019)
- [Token Sequence Labeling vs. Clause Classification for English Emotion Stimulus Detection](https://mlsanity.com/paper/2010.07557/) (2020)
- [An Experimental Study of The Effects of Position Bias on Emotion CauseExtraction](https://mlsanity.com/paper/2007.15066/) (2020)

## Built on by (most cited first, 17 of 17)

- [Reducing Position Bias in Simultaneous Machine Translation with Length-Aware Framework](https://mlsanity.com/paper/2203.09053/) (2022)
- [Knowledge-Bridged Causal Interaction Network for Causal Emotion Entailment](https://mlsanity.com/paper/2212.02995/) (2022)
- [Neutral Utterances are Also Causes: Enhancing Conversational Causal Emotion Entailment with Social Commonsense Knowledge](https://mlsanity.com/paper/2205.00759/) (2022)
- [Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction](https://mlsanity.com/paper/2209.04112/) (2022)
- [Sentiment-oriented Transformer-based Variational Autoencoder Network for Live Video Commenting](https://mlsanity.com/paper/2404.12782/) (2024)
- [TSAM: A Two-Stream Attention Model for Causal Emotion Entailment](https://mlsanity.com/paper/2203.00819/) (2022)
- [Hierarchical Interpretation of Neural Text Classification](https://mlsanity.com/paper/2202.09792/) (2022)
- [Debiasing Stance Detection Models with Counterfactual Reasoning and Adversarial Bias Learning](https://mlsanity.com/paper/2212.10392/) (2022)
- [Emotion Prediction Oriented method with Multiple Supervisions for Emotion-Cause Pair Extraction](https://mlsanity.com/paper/2302.12417/) (2023)
- [Natural Language Processing for Cognitive Analysis of Emotions](https://mlsanity.com/paper/2210.05296/) (2022)
- [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)
- [PAGE: A Position-Aware Graph-Based Model for Emotion Cause Entailment in Conversation](https://mlsanity.com/paper/2303.01795/) (2023)
- [A Bipartite Graph is All We Need for Enhancing Emotional Reasoning with Commonsense Knowledge](https://mlsanity.com/paper/2308.04811/) (2023)
- [A Multi-turn Machine Reading Comprehension Framework with Rethink Mechanism for Emotion-Cause Pair Extraction](https://mlsanity.com/paper/2209.07972/) (2022)
- [Span-level Emotion-Cause-Category Triplet Extraction with Instruction Tuning LLMs and Data Augmentation](https://mlsanity.com/paper/2504.12331/) (2025)
- [They Are Not the Same: Direct Causes Are Not Grounded Emotion Explanations](https://mlsanity.com/paper/2605.25208/) (2026)

## Abstract (arXiv)

The Emotion Cause Extraction (ECE)} task aims to identify clauses which contain emotion-evoking information for a particular emotion expressed in text. We observe that a widely-used ECE dataset exhibits a bias that the majority of annotated cause clauses are either directly before their associated emotion clauses or are the emotion clauses themselves. Existing models for ECE tend to explore such relative position information and suffer from the dataset bias. To investigate the degree of reliance of existing ECE models on clause relative positions, we propose a novel strategy to generate adversarial examples in which the relative position information is no longer the indicative feature of cause clauses. We test the performance of existing models on such adversarial examples and observe a significant performance drop. To address the dataset bias, we propose a novel graph-based method to explicitly model the emotion triggering paths by leveraging the commonsense knowledge to enhance the semantic dependencies between a candidate clause and an emotion clause. Experimental results show that our proposed approach performs on par with the existing state-of-the-art methods on the original ECE dataset, and is more robust against adversarial attacks compared to existing models.