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

Yan, Hanqi; Gui, Lin; Pergola, Gabriele; He, Yulan

doi:10.18653/v1/2021.acl-long.261

Cited by 33 publications

(14 citation statements)

References 22 publications

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“…Aiming at the unbalanced distribution of datasets [15], Yan et al [16] studied the existing ECE methods and found the dependence of the model on the relative position of clauses. ey proposed a novel strategy to generate adversarial examples in which the relative position information is no longer the indicative feature of cause clauses.…”

Section: Emotion Cause Extraction By Deep Learning Methodmentioning

confidence: 99%

“…(vi) FSS-GCN: Hu et al [14] proposed an emotion cause extraction method based on GCN, which could automatically learn and select relevant clauses useful for the task. (vii) KAG: Yan et al [16] proposed a novel graphbased 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.…”

Section: Comparison With Existing Methodsmentioning

confidence: 99%

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Hierarchical Network Emotional Assistance Mechanism for Emotion Cause Extraction

Wang

Wei

Ruan

et al. 2022

Security and Communication Networks

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The fine-grained task of emotion analysis, emotion cause extraction, is a current research hotspot. It aims to discover the underlying reasons behind the emotional expression in texts. Most of the existing work regards the task as an independent text clause classification problem, ignoring the relationship between the clauses and failing to use the indicative relationship between emotional sentences and emotional cause sentences. The existence of these problems greatly affects the accuracy of the task. In this work, an emotion cause extraction method based on a hierarchical network emotional assistance mechanism is proposed. This method uses a hierarchical network composed of bidirectional gated recurrent units, attention mechanism, and graph convolutional networks to capture clause context information, deep semantic information, and structural information between clause neighborhoods. At the same time, by enhancing the emotional information representation of the graph convolutional network nodes, the clause features of the text emotional keywords are introduced into the discovery of candidate cause sentences. Thus, a model of the deep neural network combined with the emotional assistance mechanism is established. Compared with the existing methods, the model established in this paper has better classification performance on the Chinese emotion cause dataset.

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Section: Emotion Cause Extraction By Deep Learning Methodmentioning

confidence: 99%

Section: Comparison With Existing Methodsmentioning

confidence: 99%

Hierarchical Network Emotional Assistance Mechanism for Emotion Cause Extraction

Wang

Wei

Ruan

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…We take the case below as an example: 6 Related Work ECE: Early works mainly exploit rule-based methods (Lee et al, 2010a,b;Chen et al, 2010) to identify the potential causes for certain emotion expressions in the text. Gui et al (2016a) first release a public annotated dataset for ECE, and based on which some feature based (Gui et al, 2016b) and neural based methods (Gui et al, 2017;Li et al, 2018;Yan et al, 2021;Li et al, 2021b) appear successively. To extract emotion and its corresponding cause jointly, first put forward the Emotion-Cause Pair Extraction (ECPE) task and tackle it by a two-step method.…”

Section: Error Analysismentioning

confidence: 99%

TSAM: A Two-Stream Attention Model for Causal Emotion Entailment

Zhang¹,

Zhang²,

Meng³

et al. 2022

Preprint

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Causal Emotion Entailment (CEE) aims to discover the potential causes behind an emotion in a conversational utterance. Previous works formalize CEE as independent utterance pair classification problems, with emotion and speaker information neglected. From a new perspective, this paper considers CEE in a joint framework. We classify multiple utterances synchronously to capture the correlations between utterances in a global view and propose a Two-Stream Attention Model (TSAM) to effectively model the speaker's emotional influences in the conversational history. Specifically, the TSAM comprises three modules: Emotion Attention Network (EAN), Speaker Attention Network (SAN), and interaction module. The EAN and SAN incorporate emotion and speaker information in parallel, and the subsequent interaction module effectively interchanges relevant information between the EAN and SAN via a mutual BiAffine transformation. Experimental results on a benchmark dataset demonstrate that our model achieves new State-Of-The-Art (SOTA) performance and outperforms baselines remarkably. 1 * This work was done when Duzhen Zhang was interning

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“…Lee et al [ 1 ] first utilized a word-level language rule system to detect causal events to solve the emotion cause extraction (ECE) task. Subsequently, many scholars [ 9 , 10 , 11 , 12 , 13 ] have used different ideas to study the ECE task. Russo et al [ 14 ] proposed to extracting the underlying causes for emotion expression correspondence based on common sense.…”

Section: Related Workmentioning

confidence: 99%

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

Cited by 33 publications

References 22 publications

Hierarchical Network Emotional Assistance Mechanism for Emotion Cause Extraction

Hierarchical Network Emotional Assistance Mechanism for Emotion Cause Extraction

TSAM: A Two-Stream Attention Model for Causal Emotion Entailment

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

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