Multimodal Emotion Recognition in Conversation (ERC) has garnered growing
attention from research communities in various fields. In this paper, we
propose a cross-modal fusion network with emotion-shift awareness (CFN-ESA) for
ERC. Extant approaches employ each modality equally without distinguishing the
amount of emotional information, rendering it hard to adequately extract
complementary and associative information from multimodal data. To cope with
this problem, in CFN-ESA, textual modalities are treated as the primary source
of emotional information, while visual and acoustic modalities are taken as the
secondary sources. Besides, most multimodal ERC models ignore emotion-shift
information and overfocus on contextual information, leading to the failure of
emotion recognition under emotion-shift scenario. We elaborate an emotion-shift
module to address this challenge. CFN-ESA mainly consists of the unimodal
encoder (RUME), cross-modal encoder (ACME), and emotion-shift module (LESM).
RUME is applied to extract conversation-level contextual emotional cues while
pulling together the data distributions between modalities; ACME is utilized to
perform multimodal interaction centered on textual modality; LESM is used to
model emotion shift and capture related information, thereby guide the learning
of the main task. Experimental results demonstrate that CFN-ESA can effectively
promote performance for ERC and remarkably outperform the state-of-the-art
models.

我们提出了一种带有情感转变意识的跨模态融合网络（CFN-ESA）用于对话中的多模态情感识别，结果显示 CFN-ESA 可以有效提高 ERC 的性能并显著优于现有模型。