Order-guided deep neural network for emotion-cause pair prediction

Fan, Wei; Zhu, Yuexuan; Wei, Ziyun; Yang, Tianyu; Ip, W. H.; Zhang, Yuxiang

doi:10.1016/j.asoc.2021.107818

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…Singh et al [45] adopted the prediction results of emotion extraction to promote the cause extraction. Considering the importance of order information, Fan et al [46] captured the sequential features of clauses through three LSTMs: forward LSTM, backward LSTM, and BiLSTM. Yang et al [47] utilized the consistency of emotion type between the emotion clause and clause pair.…”

Section: End-to-end Ecpementioning

confidence: 99%

A Hierarchical Heterogeneous Graph Attention Network for Emotion-Cause Pair Extraction

Liu

et al. 2022

Electronics

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Recently, graph neural networks (GNN), due to their compelling representation learning ability, have been exploited to deal with emotion-cause pair extraction (ECPE). However, current GNN-based ECPE methods mostly concentrate on modeling the local dependency relation between homogeneous nodes at the semantic granularity of clauses or clause pairs, while they fail to take full advantage of the rich semantic information in the document. To solve this problem, we propose a novel hierarchical heterogeneous graph attention network to model global semantic relations among nodes. Especially, our method introduces all types of semantic elements involved in the ECPE, not just clauses or clause pairs. Specifically, we first model the dependency between clauses and words, in which word nodes are also exploited as an intermediary for the association between clause nodes. Secondly, a pair-level subgraph is constructed to explore the correlation between the pair nodes and their different neighboring nodes. Representation learning of clauses and clause pairs is achieved by two-level heterogeneous graph attention networks. Experiments on the benchmark datasets show that our proposed model achieves a significant improvement over 13 compared methods.

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Section: End-to-end Ecpementioning

confidence: 99%

A Hierarchical Heterogeneous Graph Attention Network for Emotion-Cause Pair Extraction

Liu

et al. 2022

Electronics

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“…Similarly, Fan et al [ 25 ] transformed each given document into a directed graph and transform the original dataset into sequences, solving the ECPE task from different perspectives. Fan et al [ 26 ] proposed an order-guided deep prediction model that integrated the different ordering between emotion clauses and cause-clauses into an end-to-end framework to tackle this task. Singh et al [ 5 ] proposed an end-to-end model for the ECPE task, and adapted the NTCIR-13 ECE corpus, and established a baseline for the ECPE task on this dataset; the experiments demonstrated the effectiveness of their model.…”

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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“…In recent years, the ECA research area has flourished significantly [90], [194], [207], [233], [234].…”

Section: Introductionmentioning

confidence: 99%

Human Emotion and Sentiment in Natural Language Understanding and Generation using Large Language Models with Limited to No Labeled Data

Riyadh¹

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Natural Language Processing (NLP) aims to utilize computational resources to comprehend and generate human language. Emotion and sentiment are integral parts of human beings, and they are often reflected in human language. Consequently, these two closely related ideas are of paramount importance to NLP. In this thesis, we focus on several NLP tasks related to human emotion and sentiment. Particularly, we focus on the domains of Sentiment Analysis and Emotion-Cause Analysis (ECA). Like most other NLP tasks, machine learning technologies are frequently leveraged to perform various NLP tasks in these two domains. A common challenge in applying machine learning technology to context-dependent tasks like Sentiment Analysis is that they require a large amount of labeled data to develop a performant model. In this thesis, we develop several techniques leveraging Transformer-based large language models (LLMs) to perform various NLP tasks within these two domains in a limited to no labeled data setting. Specifically, we devise two technical architectures to perform multi-class Sentiment Analysis with limited labeled data. We introduce two new NLP tasks within the domain of ECA, which are also the first Natural Language Generation (NLG) tasks in this domain. We devise technical solutions to perform these NLG tasks, one with limited labeled data, and the other with no labeled data. We publish a new dataset for one of these novel NLG tasks. Lastly, we propose leveraging conversational LLMs for the automatic evaluation of open-ended NLG tasks, which also does not require any new training or labeled data.

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Order-guided deep neural network for emotion-cause pair prediction

Cited by 7 publications

References 23 publications

A Hierarchical Heterogeneous Graph Attention Network for Emotion-Cause Pair Extraction

A Hierarchical Heterogeneous Graph Attention Network for Emotion-Cause Pair Extraction

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

Human Emotion and Sentiment in Natural Language Understanding and Generation using Large Language Models with Limited to No Labeled Data

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