@inproceedings{zhang-etal-2024-message,
title = "Message Passing on Semantic-Anchor-Graphs for Fine-grained Emotion Representation Learning and Classification",
author = "Zhang, Pinyi and
Chen, Jingyang and
Shen, Junchen and
Zhai, Zijie and
Li, Ping and
Zhang, Jie and
Zhang, Kai",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.162",
doi = "10.18653/v1/2024.emnlp-main.162",
pages = "2771--2783",
abstract = "Emotion classification has wide applications in education, robotics, virtual reality, etc. However, identifying subtle differences between fine-grained emotion categories remains challenging. Current methods typically aggregate numerous token embeddings of a sentence into a single vector, which, while being an efficient compressor, may not fully capture complex semantic and temporal distributions. To solve this problem, we propose SEmantic ANchor Graph Neural Networks (SEAN-GNN) for fine-grained emotion classification. It learns a group of representative, multi-faceted semantic anchors in the token embedding space: using these anchors as a global reference, any sentence can be projected onto them to form a {``}semantic-anchor graph{''}, with node attributes and edge weights quantifying the semantic and temporal information respectively. The graph structure is well aligned across sentences and, importantly, allows for generating comprehensive emotion representations regarding $K$ different anchors. Message passing on this graph can further integrate and refine the learned features. Empirically, SEAN-GNN can generate meaningful semantic anchors and discriminative graph patterns for different emotion, with promising classification results on 6 popular benchmark datasets against state-of-the-arts.",
}
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<abstract>Emotion classification has wide applications in education, robotics, virtual reality, etc. However, identifying subtle differences between fine-grained emotion categories remains challenging. Current methods typically aggregate numerous token embeddings of a sentence into a single vector, which, while being an efficient compressor, may not fully capture complex semantic and temporal distributions. To solve this problem, we propose SEmantic ANchor Graph Neural Networks (SEAN-GNN) for fine-grained emotion classification. It learns a group of representative, multi-faceted semantic anchors in the token embedding space: using these anchors as a global reference, any sentence can be projected onto them to form a “semantic-anchor graph”, with node attributes and edge weights quantifying the semantic and temporal information respectively. The graph structure is well aligned across sentences and, importantly, allows for generating comprehensive emotion representations regarding K different anchors. Message passing on this graph can further integrate and refine the learned features. Empirically, SEAN-GNN can generate meaningful semantic anchors and discriminative graph patterns for different emotion, with promising classification results on 6 popular benchmark datasets against state-of-the-arts.</abstract>
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%0 Conference Proceedings
%T Message Passing on Semantic-Anchor-Graphs for Fine-grained Emotion Representation Learning and Classification
%A Zhang, Pinyi
%A Chen, Jingyang
%A Shen, Junchen
%A Zhai, Zijie
%A Li, Ping
%A Zhang, Jie
%A Zhang, Kai
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F zhang-etal-2024-message
%X Emotion classification has wide applications in education, robotics, virtual reality, etc. However, identifying subtle differences between fine-grained emotion categories remains challenging. Current methods typically aggregate numerous token embeddings of a sentence into a single vector, which, while being an efficient compressor, may not fully capture complex semantic and temporal distributions. To solve this problem, we propose SEmantic ANchor Graph Neural Networks (SEAN-GNN) for fine-grained emotion classification. It learns a group of representative, multi-faceted semantic anchors in the token embedding space: using these anchors as a global reference, any sentence can be projected onto them to form a “semantic-anchor graph”, with node attributes and edge weights quantifying the semantic and temporal information respectively. The graph structure is well aligned across sentences and, importantly, allows for generating comprehensive emotion representations regarding K different anchors. Message passing on this graph can further integrate and refine the learned features. Empirically, SEAN-GNN can generate meaningful semantic anchors and discriminative graph patterns for different emotion, with promising classification results on 6 popular benchmark datasets against state-of-the-arts.
%R 10.18653/v1/2024.emnlp-main.162
%U https://aclanthology.org/2024.emnlp-main.162
%U https://doi.org/10.18653/v1/2024.emnlp-main.162
%P 2771-2783
Markdown (Informal)
[Message Passing on Semantic-Anchor-Graphs for Fine-grained Emotion Representation Learning and Classification](https://aclanthology.org/2024.emnlp-main.162) (Zhang et al., EMNLP 2024)
ACL
- Pinyi Zhang, Jingyang Chen, Junchen Shen, Zijie Zhai, Ping Li, Jie Zhang, and Kai Zhang. 2024. Message Passing on Semantic-Anchor-Graphs for Fine-grained Emotion Representation Learning and Classification. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 2771–2783, Miami, Florida, USA. Association for Computational Linguistics.