AutoML-Emo: Automatic Knowledge Selection Using Congruent Effect for Emotion Identification in Conversations

Jiang, Dazhi; Wei, Runguo; Wen, Jintao; Tu, Geng; Wang, Zhaoxia

doi:10.1109/taffc.2022.3232166

Cited by 8 publications

(7 citation statements)

References 51 publications

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“…Traditional methods treated the sentiment analysis tasks as a sentiment classification [36][37][38]. These methods primarily focused on categorizing text into discrete sentiment labels, such as positive, negative, or neutral.…”

Section: Continuous Emotional Spacementioning

confidence: 99%

Enhancing Grid Tagging Schema with Hybrid Contrastive Learning for Aspect-level Sentiment Triplet Extraction

Zheng,

Wang,

et al. 2023

Preprint

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Aspect-based sentiment triplet extraction (ASTE) is a fine-grained opinion mining to jointly extract a triplet consisting of aspect terms, opinion terms, and sentiment polarity. Previous works tried incorporating extra dependency information by stacking complex graph convolution networks (GCN) on transformers to learn the relationship between the aspects and their associated opinion. While the number of existing datasets is still insufficient, the GCN-accompanied redundant parameters in each method require more samples to provide supervised information, leading to the model being trapped by overfitting. Instead of stacking GCNs, this study proposes an enhanced grid tagging scheme with hybrid contrastive learning, exploiting a self-supervised training approach from the negative samples. Specifically, sentiment-aware contrastive learning (SCL) is applied by introducing word-level continuous sentiment information in the valence-arousal space. Syntactic-aware graph contrastive learning (SGCL) is used to guide the language model to gain the ability to capture dependencies. Supervised tagging contrastive learning (STCL) is further adapted to guide the language model to distinguish both positive and negative samples. Hybrid contrastive learning can provide extra supervision information but only introduce tolerable parameters. Extensive experimental results on the ASTE-Data-V2 datasets show that the proposed model significantly outperforms previous baselines.

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Section: Continuous Emotional Spacementioning

confidence: 99%

Enhancing Grid Tagging Schema with Hybrid Contrastive Learning for Aspect-level Sentiment Triplet Extraction

Zheng,

Wang,

et al. 2023

Preprint

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show abstract

“…Out of the 50 submissions received, only 9 were accepted to appear in this special issue. All articles have been reviewed by at least three reviewers and handled by the guest editors, except for [11] and [12], which were handled independently by other Associate Editors of the journal. Among the selected articles, [11], [13], [14] belong to the first direction of interpretability and explainability; [12], [15], [16] belong to the second direction of injecting external knowledge into a neural model; finally, [17], [18], [19] belong to the third direction of migrating theory from reference disciplines.…”

Section: Contents Of This Special Issuementioning

confidence: 99%

Guest Editorial Neurosymbolic AI for Sentiment Analysis

Xing,

Schuller,

Chaturvedi

et al. 2023

IEEE Trans. Affective Comput.

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“…Emotion recognition in conversations (ERC) has received considerable attention [14,37,38] due to its potential applications in several areas, like recommendation systems and dialogue generation [36].…”

Section: Introductionmentioning

confidence: 99%

Do Topic and Causal Consistency Affect Emotion Cognition? A Graph Interactive Network for Conversational Emotion Detection

Tu,

Liang,

Lyu

et al. 2023

Frontiers in Artificial Intelligence and Applications

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Emotion recognition in conversations (ERC) typically requires modeling both intra- and inter-speaker context dependencies. However, when modeling inter-speaker dependencies, it may not capture differences among other participants in the conversation. Recent ERC research has attempted to improve utterance representations by utilizing speakers’ commonsense knowledge. Nonetheless, these studies ignore the causal consistency in knowledge between the two participants, which contradicts the above modeling of speaker-sensitive context dependencies. Additionally, it is observed that historical utterances from various topics are blindly leveraged in context modeling, which fails the inter- and intra-topic coherence. To address these issues, we propose the topic- and causal-aware interactive graph network (TCA-IGN). Specifically, we suggest a graph encoder to model topic-level context dependencies, achieving inter- and intra-topic coherence. The topics of utterances are derived from a context-sensitive neural topic model. Then, we present a causal-aware graph attention to keep the speaker’s causal consistency in commonsense knowledge, improving speaker-level context modeling. Finally, considering the defect of modeling inter-speaker or inter-topic context dependencies, we employ supervised contrastive learning to sweeten it. Experimental results show that TCA-IGN outperforms state-of-the-art methods on three public conversational datasets.

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AutoML-Emo: Automatic Knowledge Selection Using Congruent Effect for Emotion Identification in Conversations

Cited by 8 publications

References 51 publications

Enhancing Grid Tagging Schema with Hybrid Contrastive Learning for Aspect-level Sentiment Triplet Extraction

Enhancing Grid Tagging Schema with Hybrid Contrastive Learning for Aspect-level Sentiment Triplet Extraction

Guest Editorial Neurosymbolic AI for Sentiment Analysis

Do Topic and Causal Consistency Affect Emotion Cognition? A Graph Interactive Network for Conversational Emotion Detection

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