End-to-end Emotion-Cause Pair Extraction via Learning to Link

Song, Haolin; Zhang, Chen; Li, Qiuchi; Song, Dawei

doi:10.48550/arxiv.2002.10710

Cited by 6 publications

(6 citation statements)

References 11 publications

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“… Inter-EC [ 2 ]: This is another interactive multi-task learning method that uses predictions from emotion extraction to reinforce cause extraction, the rest of the model is the same as Indep. E2EECPE: An end-to-end model proposed by Song et al [ 7 ], this is a multi-task learning linking framework that exploits a biaffine attention to mine the relationship between any two clauses. ECPE-2D: Proposed by Ding et al [ 8 ], tthis model realizes all the interactions of emotion-cause pairs in 2D, and uses the self-attention mechanism to calculate the attention matrix of emotion-cause pairs.…”

Section: Methodsmentioning

confidence: 99%

“…E2EECPE: An end-to-end model proposed by Song et al [ 7 ], this is a multi-task learning linking framework that exploits a biaffine attention to mine the relationship between any two clauses.…”

Section: Methodsmentioning

confidence: 99%

“…In response to this situation, some scholars [ 3 , 4 , 5 , 6 ] proposed to use an end-to-end model to solve the ECPE task. Song et al [ 7 ] used an end-to-end multi-task learning connection framework to solve this problem, regarding the ECPE task as a link prediction problem, and used the biaffine attention mechanism to judge whether there was a directional link between emotion and cause. Ding et al [ 8 ] represented emotion-cause pairs in two dimensions and predicted them.…”

Section: Introductionmentioning

confidence: 99%

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Deep Neural Networks Based on Span Association Prediction for Emotion-Cause Pair Extraction

Huang

Yang

Peng

et al. 2022

Sensors

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The emotion-cause pair extraction task is a fine-grained task in text sentiment analysis, which aims to extract all emotions and their underlying causes in a document. Recent studies have addressed the emotion-cause pair extraction task in a step-by-step manner, i.e., the two subtasks of emotion extraction and cause extraction are completed first, followed by the pairing task of emotion-cause pairs. However, this fail to deal well with the potential relationship between the two subtasks and the extraction task of emotion-cause pairs. At the same time, the grammatical information contained in the document itself is ignored. To address the above issues, we propose a deep neural network based on span association prediction for the task of emotion-cause pair extraction, exploiting general grammatical conventions to span-encode sentences. We use the span association pairing method to obtain candidate emotion-cause pairs, and establish a multi-dimensional information interaction mechanism to screen candidate emotion-cause pairs. Experimental results on a quasi-baseline corpus show that our model can accurately extract potential emotion-cause pairs and outperform existing baselines.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Neural Networks Based on Span Association Prediction for Emotion-Cause Pair Extraction

Huang

Yang

Peng

et al. 2022

Sensors

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show abstract

“…E2EECPE: This end-to-end framework was proposed by Study [13]. Biaffine attention is utilized to predict the causal relationship between clauses, Song transforming the clause pairing problem into the edge prediction problem.…”

Section: Compared Ecpe Modelsmentioning

confidence: 99%

SEE-3D: Sentiment-driven emotion-cause pair extraction based on 3D-CNN

Zhang

et al. 2023

COMSIS J

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As an emotional cause detection task, Emotion-Cause Pair Extraction (ECPE) provides technical support for intelligent psychological counseling, empty-nest elderly care, and other fields. Current approaches mainly focus on extracting by recognizing causal relationships between clauses. Different from these existing methods, this paper further considers the influence of sentimental intensity to improve extraction accuracy. To address this issue, we propose an extraction model based on sentiment analysis and 3D Convolutional Neural Networks (3D-CNN), named SEE-3D. First, to prepare fundamental data for sentiment analysis, emotion clauses are clustered into six emotion domains according to six emotion types in the ECPE dataset. Then, a pre-trained sentiment analysis model is introduced to compute emotional similarity, which provides a reference for identifying emotion clauses. In the extraction process, similar features of adjacent documents in the same batch of samples are fused as input of 3D-CNN. The 3D-CNN enhances the macro semantic understanding ability of the model, thereby improving the extraction performance. The results of experiments show that the accuracy of ECPE can be effectively improved by the SEE-3D model.

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“…The first set of work for an end-to-end architecture was done by Ding et al [11], where the authors used a representation scheme (in 2D) to represent emotion cause clause pairs and then integrated the cause and emotion pair interaction, prediction, and representation into a single combined framework. Song et al [34] and Fan et al [13] solved this problem using a graph-based approach to recognize emotions and their corresponding causes. Chen et al [5] described this problem as a unified sequence labeling problem, where they extract emotion cause pairs using CNNs.…”

Section: Related Workmentioning

confidence: 99%

Multi-Task Learning Framework for Extracting Emotion Cause Span and Entailment in Conversations

Bhat¹,

Modi²

2022

Preprint

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Predicting emotions expressed in text is a well-studied problem in the NLP community. Recently there has been active research in extracting the cause of an emotion expressed in text. Most of the previous work has done causal emotion entailment in documents. In this work, we propose neural models to extract emotion cause span and entailment in conversations. For learning such models, we use RECCON dataset, which is annotated with cause spans at the utterance level. In particular, we propose MuTEC, an end-to-end Multi-Task learning framework for extracting emotions, emotion cause, and entailment in conversations. This is in contrast to existing baseline models that use ground truth emotions to extract the cause. MuTEC performs better than the baselines for most of the data folds provided in the dataset.

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End-to-end Emotion-Cause Pair Extraction via Learning to Link

Cited by 6 publications

References 11 publications

Deep Neural Networks Based on Span Association Prediction for Emotion-Cause Pair Extraction

Deep Neural Networks Based on Span Association Prediction for Emotion-Cause Pair Extraction

SEE-3D: Sentiment-driven emotion-cause pair extraction based on 3D-CNN

Multi-Task Learning Framework for Extracting Emotion Cause Span and Entailment in Conversations

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