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首页> 外文会议>China National Conference on Computational Linguistics >Multimodal Sentiment Analysis with Multi-perspective Fusion Network Focusing on Sense Attentive Language
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Multimodal Sentiment Analysis with Multi-perspective Fusion Network Focusing on Sense Attentive Language

机译：多透视融合网络专注于易感细节语言的多模式情绪分析

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摘要

Multimodal sentiment analysis aims to learn a joint representation of multiple features. As demonstrated by previous studies, it is shown that the language modality may contain more semantic information than that of other modalities. Based on this observation, we propose a Multi-perspective Fusion Network (MPFN) focusing on Sense Attentive Language for multimodal sentiment analysis. Different from previous studies, we use the language modality as the main part of the final joint representation, and propose a multi-stage and uni-stage fusion strategy to get the fusion representation of the multiple modalities to assist the final language-dominated multimodal representation. In our model, a Sense-Level Attention Network is proposed to dynamically learn the word representation which is guided by the fusion of the multiple modalities. As in turn, the learned language representation can also help the multi-stage and uni-stage fusion of the different modalities. In this way, the model can jointly learn a well integrated final representation focusing on the language and the interactions between the multiple modalities both on multi-stage and uni-stage. Several experiments are carried on the CMU-MOSI, the CMU-MOSEI and the YouTube public datasets. The experiments show that our model performs better or competitive results compared with the baseline models.
机译：多模式情绪分析旨在学习多种特征的联合代表。如先前研究所证明的，示出语言模态可以包含比其他方式的语义信息更多。基于此观察，我们提出了一种多模式情绪分析的多视角融合网络（MPFN），专注于易感细心语言。与以前的研究不同，我们使用语言模态作为最终联合表示的主要部分，并提出了一个多级和Uni-阶段的融合策略，以获得多种方式的融合表示，以协助最终的语言主导的多式联数代表。在我们的模型中，提出了一种感觉级别的关注网络来动态地学习由多种模式的融合引导的单词表示。依次，读取的语言表示还可以帮助不同方式的多阶段和Uni-阶段融合。以这种方式，该模型可以共同学习专注于语言的良好集成的最终表示，以及多级和Uni-阶段的多种模式之间的交互。在CMU-MOSI，CMU-MOSEI和YouTube公共数据集上进行了几个实验。实验表明，与基线模型相比，我们的模型表现更好或更有竞争力的结果。

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