# From Independent Prediction to Re-ordered Prediction: Integrating Relative Position and Global Label Information to Emotion Cause Identification

arXiv: https://arxiv.org/abs/1906.01230 · HTML: https://mlsanity.com/paper/1906.01230/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2019.

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

- [Is ChatGPT a Good Sentiment Analyzer? A Preliminary Study](https://mlsanity.com/paper/2304.04339/) (2023)
- [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)
- [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)
- [SemEval-2024 Task 3: Multimodal Emotion Cause Analysis in Conversations](https://mlsanity.com/paper/2405.13049/) (2024)
- [Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction](https://mlsanity.com/paper/2209.04112/) (2022)
- [TSAM: A Two-Stream Attention Model for Causal Emotion Entailment](https://mlsanity.com/paper/2203.00819/) (2022)
- [Emotion Prediction Oriented method with Multiple Supervisions for Emotion-Cause Pair Extraction](https://mlsanity.com/paper/2302.12417/) (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)
- [Discourse-Aware Emotion Cause Extraction in Conversations](https://mlsanity.com/paper/2210.14419/) (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 identification aims at identifying the potential causes that lead to a certain emotion expression in text. Several techniques including rule based methods and traditional machine learning methods have been proposed to address this problem based on manually designed rules and features. More recently, some deep learning methods have also been applied to this task, with the attempt to automatically capture the causal relationship of emotion and its causes embodied in the text. In this work, we find that in addition to the content of the text, there are another two kinds of information, namely relative position and global labels, that are also very important for emotion cause identification. To integrate such information, we propose a model based on the neural network architecture to encode the three elements (i.e., text content, relative position and global label), in an unified and end-to-end fashion. We introduce a relative position augmented embedding learning algorithm, and transform the task from an independent prediction problem to a reordered prediction problem, where the dynamic global label information is incorporated. Experimental results on a benchmark emotion cause dataset show that our model achieves new state-of-the-art performance and performs significantly better than a number of competitive baselines. Further analysis shows the effectiveness of the relative position augmented embedding learning algorithm and the reordered prediction mechanism with dynamic global labels.