Challenge: Emotion-cause pair extraction is a task that aims to extract emotions and the events causing such emotions.
Approach: They propose a deep latent model which captures the underlying latent structures of data and utilizes the easily transferable knowledge of emotions as the bridge to link the distributions of events in different domains.
Outcome: The proposed model outperforms the strongest baseline by approximately 11.05% on a Chinese benchmark and 2.45% on an English benchmark in terms of weighted-average F1 score.

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Transition-based Directed Graph Construction for Emotion-Cause Pair Extraction (2020.acl-main)

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Challenge: Existing methods to extract emotions and causes from unannotated text are pipelined, causing error propagation.
Approach: They propose to transform a task into a procedure of parsing-like directed graph construction . they propose to generate a directed graph with labeled edges based on a sequence of actions .
Outcome: The proposed method outperforms the state-of-the-art methods by 6.71% (p0.01) in F1 measure.
Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts (P19-1)

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Challenge: Emotion cause extraction (ECE) aims at extracting potential causes behind certain emotions in text.
Approach: They propose a 2-step task to extract potential pairs of emotions and corresponding causes in a document.
Outcome: The proposed task is based on a benchmark emotion cause corpus.
Always the Best Fit: Adaptive Domain Gap Filling from Causal Perspective for Few-Shot Relation Extraction (2023.findings-emnlp)

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Challenge: Existing approaches to cross-domain relation extraction have been limited by domains . data bias between domains can be difficult to fill, especially in few-shot scenarios .
Approach: They propose a framework to bridge the semantic gap caused by data bias between domains . they use syntactic structure, label distribution, and entities to calculate causal effects .
Outcome: The proposed framework fills the domain gap and yields better results on the few-shot task.
End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network (2020.coling-main)

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Challenge: Emotion-cause pair extraction (ECPE) aims to extract emotion expressions and their corresponding causes in a document simultaneously.
Approach: They propose to model pair-level contexts so that to capture dependency information among local neighborhood candidate pairs.
Outcome: The proposed model extracts emotion-cause pairs and their causes from documents . it is based on a benchmark Chinese emotion-case pair extraction corpus .
Unsupervised Relation Extraction: A Variational Autoencoder Approach (2021.emnlp-main)

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Challenge: Existing methods for relation extraction use latent variables and supervised training which requires large datasets.
Approach: They propose a VAE-based unsupervised relation extraction technique that uses latent variables as an intermediate variable instead of a latent variable.
Outcome: The proposed method outperforms state-of-the-art methods on the NYT dataset and outperformed existing methods.
Emotion-Cause Pair Extraction as Sequence Labeling Based on A Novel Tagging Scheme (2020.emnlp-main)

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Challenge: Existing methods to extract emotions and causes from unannotated emotion texts are labor intensive and limited applications in real-world scenarios.
Approach: They propose a novel task to find emotions and corresponding causes in unannotated emotion texts.
Outcome: The proposed model outperforms the state-of-the-art method by 2.26% (p0.001) in F1 measure.
Emotion–Cause Pair Extraction in Conversations via Semantic Decoupling and Alignment (2026.findings-acl)

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Challenge: Emotion-Cause Pair Extraction in Conversations (ECPEC) aims to identify the set of causal relations between emotion utterances and their triggering causes within a dialogue.
Approach: They propose a framework for Emotion-Cause Pair Extraction in Conversations that decouples emotion-oriented semantics from cause-oriented ones and employs optimal transport to enable many-to-many and globally consistent emotion-cause matching.
Outcome: The proposed framework achieves state-of-the-art on several benchmark datasets.
Projecting Embeddings for Domain Adaption: Joint Modeling of Sentiment Analysis in Diverse Domains (C18-1)

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Challenge: Existing domain adaptation methods for sentiment analysis are sensitive to domain differences, resulting in classifiers that perform poorly on new domains.
Approach: They propose a domain adaptation problem as an embedding projection task using two mono-domain embeddable spaces and a bi-domain space to project across domains and predict sentiment.
Outcome: The proposed model performs better on domains similar to state-of-the-art methods while requiring longer training times.
Joint Constrained Learning with Boundary-adjusting for Emotion-Cause Pair Extraction (2023.acl-long)

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Challenge: Emotion-Cause Pair Extraction (ECPE) aims to identify the document’s emotion clauses and corresponding cause clauses.
Approach: They propose a constrained learning framework with boundary-adjusting for Emotion-Cause Pair Extraction that summarizes prior rules and forces the model to take them into consideration in optimization.
Outcome: The proposed framework achieves competitive results compared with state-of-the-art methods on unbalanced data and proves robustness on unbalancing data.
Causal-LLM: A Unified One-Shot Framework for Prompt- and Data-Driven Causal Graph Discovery (2025.findings-emnlp)

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Challenge: Current causal discovery methods rely on pairwise or iterative strategies that fail to capture global dependencies, amplify local biases, and reduce overall accuracy.
Approach: They propose a framework for one-step full causal graph discovery using prompt-based discovery and a data-driven method for settings without metadata.
Outcome: The proposed framework outperforms state-of-the-art models by approximately 40% in edge accuracy on datasets like Asia and Sachs while maintaining strong performance on more complex graphs.

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