arXiv:2010.02057
공식 코드 · Papers with Code
Modulated Fusion using Transformer for Linguistic-Acoustic Emotion Recognition
원문 초록
This paper aims to bring a new lightweight yet powerful solution for the task of Emotion Recognition and Sentiment Analysis. Our motivation is to propose two architectures based on Transformers and modulation that combine the linguistic and acoustic inputs from a wide range of datasets to challenge, and sometimes surpass, the state-of-the-art in the field. To demonstrate the efficiency of our models, we carefully evaluate their performances on the IEMOCAP, MOSI, MOSEI and MELD dataset. The experiments can be directly replicated and the code is fully open for future researches.
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Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition
Hierarchical Adaptive Expert for Multimodal Sentiment Analysis
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Analyzing Emotions in Bangla Social Media Comments Using Machine Learning and LIME
An Empirical Study on Configuring In-Context Learning Demonstrations for Unleashing MLLMs' Sentimental Perception Capability
Emotion Recognition for Low-Resource Turkish: Fine-Tuning BERTurk on TREMO and Testing on Xenophobic Political Discourse
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