论文标题
混合多模式融合用于幽默检测
Hybrid Multimodal Fusion for Humor Detection
论文作者
论文摘要
在本文中，我们将解决方案介绍给2022年多模式情感挑战（MUSE）的Muse-Humor子挑战。Muse-Humor Sub-Challenge的目的是发现幽默，并根据德国足球大卫的听觉录音来计算AUC。它是针对教练表现出的幽默的注释。对于此子挑战，我们首先使用变压器模块和BilstM模块构建一个判别模型，然后提出一种混合融合策略，以使用每种模态的预测结果来提高模型的性能。我们的实验证明了我们提出的模型和混合融合策略对多模式融合的有效性，并且我们在测试集上提出的模型的AUC为0.8972。
In this paper, we present our solution to the MuSe-Humor sub-challenge of the Multimodal Emotional Challenge (MuSe) 2022. The goal of the MuSe-Humor sub-challenge is to detect humor and calculate AUC from audiovisual recordings of German football Bundesliga press conferences. It is annotated for humor displayed by the coaches. For this sub-challenge, we first build a discriminant model using the transformer module and BiLSTM module, and then propose a hybrid fusion strategy to use the prediction results of each modality to improve the performance of the model. Our experiments demonstrate the effectiveness of our proposed model and hybrid fusion strategy on multimodal fusion, and the AUC of our proposed model on the test set is 0.8972.
