Papers with Emotion cause analysis
Boundary Detection with BERT for Span-level Emotion Cause Analysis (2021.findings-acl)
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| Challenge: | Emotion cause analysis (ECA) is an emerging topic in natural language processing, which aims to identify the reasons behind a given emotion. |
| Approach: | They propose to detect the precise boundaries of text spans conveying accurate emotion causes from the given context by a sequence labeling and position identification problem. |
| Outcome: | The proposed methods outperform existing models on two benchmark datasets on the emotion cause analysis task. |
One Unified Model for Diverse Tasks: Emotion Cause Analysis via Self-Promote Cognitive Structure Modeling (2025.naacl-long)
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| Challenge: | Existing models for emotion cause analysis overlook common ground rooted in cognitive emotion theories, in particular, the cognitive structure of emotions. |
| Approach: | They propose a unified model capable of tackling diverse emotion cause analysis tasks . they propose 'self-promote mechanism' that constructs the emotion cognitive structure through LLM . |
| Outcome: | The proposed model outperforms existing models and baselines on multiple emotion cause analysis tasks. |
A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis (D19-1)
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| Challenge: | Emotion cause analysis aims to identify the reasons behind emotions . previous models focus on learning architecture with local textual information . |
| Approach: | They propose a method to extract emotion cause with hierarchical neural model and knowledge-based regularizations by sentiment lexicon and common knowledge. |
| Outcome: | The proposed method outperforms baselines on two public datasets in different languages and outperformed competitive baselines by 2.08%. |
UECA-Prompt: Universal Prompt for Emotion Cause Analysis (2022.coling-1)
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| Challenge: | Existing methods adopt fine-tuning paradigm to solve certain types of ECA tasks. Existing models suffer from dataset bias. |
| Approach: | They propose a universal prompt tuning method to solve different ECA tasks in a unified framework and a sequential learning module to ease the dataset bias. |
| Outcome: | The proposed method achieves competitive performance on the ECA datasets. |