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 | ???core.languages.en_GB??? |
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頁(從 - 到) | 109974-109982 |
頁數 | 9 |
期刊 | IEEE Access |
卷 | 11 |
DOIs | |
出版狀態 | 已出版 - 2023 |