TF-BERT:基于张量器的融合BERT用于多式联网情绪分析
Jingming Hou1, Nazlia Omar1, Sabrina Tiun1
1Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia.
概括
本研究介绍了基于张量器的融合BERT (TF-BERT) 用于多式联络情绪分析,克服了仅处理两个模式的局限性. TF-BERT通过同时处理三种模式来提高准确性来增强情绪数据融合.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 计算机视觉 计算机视觉
背景情况:
- 单模式情感分析与现实世界的复杂性作斗争.
- 现有的用于多式联络情绪分析的变压器模型仅限于同时处理两个模式.
- 这种限制导致信息交换不足和情绪数据的潜在损失.
研究的目的:
- 提出一种新的基于电压器的融合BERT (TF-BERT) 模型,以解决传统交叉模式变压器模型的局限性.
- 在多式联络情绪分析中增强信息交换和情绪数据表示.
- 允许同时处理三种模式,以便进行更全面的分析.
主要方法:
- 开发了集成到BERT的基于张量器的跨模融合 (TCF) 模块.
- 引入了基于子的交叉模式变压器 (TCT) 模块,用于同时处理三种模式.
- 将TCF嵌入到BERT的变压器的多层中,以实现渐进,动态的模式补充.
主要成果:
- 在大多数指标中,TF-BERT在CMU-MOSI和CMU-MOSEI数据集上取得了最先进的结果.
- 废弃性研究证实了TCF和TCT模块的有效性.
- 该模型在逐步整合和捕捉所有模式的复杂情感互动方面表现出卓越的表现.
结论:
- TF-BERT有效地克服了多式联运情绪分析中传统模型的局限性.
- 拟议的TCF和TCT模块显著改善了信息交换和情感表现.
- TF-BERT提供了一种更强大,更全面的方法来分析多式联络数据中的复杂情感互动.
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