Toward Transformer Fusions for Chinese Sentiment Intensity Prediction in Valence-Arousal Dimensions

Yu Chih Deng, Yih-Ru Wang, Sin-Horng Chen, Lung Hao Lee*

*此作品的通信作者

研究成果: Article同行評審

6 引文 斯高帕斯(Scopus)

摘要

BERT (Bidirectional Encoder Representations from Transformers) uses an encoder architecture with an attention mechanism to construct a transformer-based neural network. In this study, we develop a Chinese word-level BERT to learn contextual language representations and propose a transformer fusion framework for Chinese sentiment intensity prediction in the valence-arousal dimensions. Experimental results on the Chinese EmoBank indicate that our transformer-based fusion model outperforms other neural-network-based, regression-based and lexicon-based methods, reflecting the effectiveness of integrating semantic representations in different degrees of linguistic granularity. Our proposed transformer fusion framework is also simple and easy to fine-tune over different downstream tasks.

原文English
頁(從 - 到)109974-109982
頁數9
期刊IEEE Access
11
DOIs
出版狀態Published - 2023

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