Hierarchically stacked graph convolution for emotion recognition in conversation

Wang, Binqiang; Dong, Gang; Zhao, Yaqian; Li, Rengang; Cao, Qichun; Hu, Kekun; Jiang, Dongdong

doi:10.1016/j.knosys.2023.110285

Cited by 17 publications

(7 citation statements)

References 28 publications

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“…• HSGCF [34]: It introduces a hierarchical structure that leverages five graph convolution layers to extract discriminative emotional features. It integrates Transformer structures to mitigate the over-smoothing problem associated with deeper networks.…”

Section: ) Baseline Methodsmentioning

confidence: 99%

Residual Relation-Aware Attention Deep Graph-Recurrent Model for Emotion Recognition in Conversation

Duong,

Ho,

Pant

et al. 2024

IEEE Access

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This work addresses Emotion Recognition in Conversation (ERC), a task with substantial implications for the classification of the underlying emotions in spoken encounters. Our focus is on utilizing a fully connected directed acyclic graph to represent conversations, presenting inter-locutor and intra-locutor ties to capture intricate relationships. Therefore, we propose a novel methodology, Residual Relation-Aware Attention (RRAA) with Positional Encoding, enhancing speaker relations' contexts for improved emotion recognition in conversation. The purpose of this mechanism is to facilitate a thorough comprehension of the connections between speakers, hence enhancing the sophistication and contextual awareness of an emotion recognition framework. We utilized the Gated recurrent units (GRU) to regulate context transmission, ensuring adaptability to changing emotional dynamics. It regulates the transmission of conversation context across all layers of the graph, guaranteeing a flexible and responsive representation of the changing emotional dynamics within the discourse. Evaluations on IEMOCAP, MELD, and EmoryNLP datasets disclose our model's superior performance (F1 scores: 69.1%, 63.82%, 39.85%, respectively), outperforming state-ofthe-art approaches. In general, this work enhances speaker interactions by utilizing a fully connected graph, and providing a more concise and efficient ERC framework. INDEX TERMSEmotion recognition in conversation, residual relation-aware attention, deep graphrecurrent model, fully connected directed acyclic graph, positional encoding, .

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Section: ) Baseline Methodsmentioning

confidence: 99%

Residual Relation-Aware Attention Deep Graph-Recurrent Model for Emotion Recognition in Conversation

Duong,

Ho,

Pant

et al. 2024

IEEE Access

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“…A multitude of techniques have been proposed for emotion recognition [40,41]. In the realm of dialogue emotion recognition, Wang [42] introduced the hierarchically stacked graph convolution framework. This framework aims to improve the extraction of discriminative information from the emotional graph it constructs.…”

Section: Literature Reviewmentioning

confidence: 99%

Switch-Transformer Sentiment Analysis Model for Arabic Dialects That Utilizes a Mixture of Experts Mechanism

Baniata,

Kang

2024

Mathematics

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In recent years, models such as the transformer have demonstrated impressive capabilities in the realm of natural language processing. However, these models are known for their complexity and the substantial training they require. Furthermore, the self-attention mechanism within the transformer, designed to capture semantic relationships among words in sequences, faces challenges when dealing with short sequences. This limitation hinders its effectiveness in five-polarity Arabic sentiment analysis (SA) tasks. The switch-transformer model has surfaced as a potential substitute. Nevertheless, when employing one-task learning for their training, these models frequently face challenges in presenting exceptional performances and encounter issues when producing resilient latent feature representations, particularly in the context of small-size datasets. This challenge is particularly prominent in the case of the Arabic dialect, which is recognized as a low-resource language. In response to these constraints, this research introduces a novel method for the sentiment analysis of Arabic text. This approach leverages multi-task learning (MTL) in combination with the switch-transformer shared encoder to enhance model adaptability and refine sentence representations. By integrating a mixture of experts (MoE) technique that breaks down the problem into smaller, more manageable sub-problems, the model becomes skilled in managing extended sequences and intricate input–output relationships, thereby benefiting both five-point and three-polarity Arabic sentiment analysis tasks. The proposed model effectively identifies sentiment in Arabic dialect sentences. The empirical results underscore its exceptional performance, with accuracy rates reaching 84.02% for the HARD dataset, 67.89% for the BRAD dataset, and 83.91% for the LABR dataset, as demonstrated by the evaluations conducted on these datasets.

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“…Numerous studies have harnessed polarity-based sentiment deep learning techniques for analyzing tweets [38,39]. A multitude of techniques have been proposed for emotion recognition [40][41][42]. In the realm of dialogue emotion recognition, Wang [43] introduced the hierarchically stacked graph convolution framework.…”

Section: Literature Reviewmentioning

confidence: 99%

Switching Self-Attention Text Classification Model with Innovative Reverse Positional Encoding for Right-to-Left Languages: A Focus on Arabic Dialects

Baniata,

Kang

2024

Mathematics

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Transformer models have emerged as frontrunners in the field of natural language processing, primarily due to their adept use of self-attention mechanisms to grasp the semantic linkages between words in sequences. Despite their strengths, these models often face challenges in single-task learning scenarios, particularly when it comes to delivering top-notch performance and crafting strong latent feature representations. This challenge is more pronounced in the context of smaller datasets and is particularly acute for under-resourced languages such as Arabic. In light of these challenges, this study introduces a novel methodology for text classification of Arabic texts. This method harnesses the newly developed Reverse Positional Encoding (RPE) technique. It adopts an inductive-transfer learning (ITL) framework combined with a switching self-attention shared encoder, thereby increasing the model’s adaptability and improving its sentence representation accuracy. The integration of Mixture of Experts (MoE) and RPE techniques empowers the model to process longer sequences more effectively. This enhancement is notably beneficial for Arabic text classification, adeptly supporting both the intricate five-point and the simpler ternary classification tasks. The empirical evidence points to its outstanding performance, achieving accuracy rates of 87.20% for the HARD dataset, 72.17% for the BRAD dataset, and 86.89% for the LABR dataset, as evidenced by the assessments conducted on these datasets.

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Hierarchically stacked graph convolution for emotion recognition in conversation

Cited by 17 publications

References 28 publications

Residual Relation-Aware Attention Deep Graph-Recurrent Model for Emotion Recognition in Conversation

Residual Relation-Aware Attention Deep Graph-Recurrent Model for Emotion Recognition in Conversation

Switch-Transformer Sentiment Analysis Model for Arabic Dialects That Utilizes a Mixture of Experts Mechanism

Switching Self-Attention Text Classification Model with Innovative Reverse Positional Encoding for Right-to-Left Languages: A Focus on Arabic Dialects

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