Leveraging Graph-based Cross-modal Information Fusion for Neural Sign Language Translation

• URL: http://arxiv.org/abs/2211.00526v1
• Date: Tue, 1 Nov 2022 15:26:22 GMT
• Title: Leveraging Graph-based Cross-modal Information Fusion for Neural Sign Language Translation
• Authors: Jiangbin Zheng, Siyuan Li, Cheng Tan, Chong Wu, Yidong Chen, Stan Z. Li
• Abstract summary: Sign Language (SL), as the mother tongue of the deaf community, is a special visual language that most hearing people cannot understand. We propose a novel neural SLT model with multi-modal feature fusion based on the dynamic graph. We are the first to introduce graph neural networks, for fusing multi-modal information, into neural sign language translation models.
• Score: 46.825957917649795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sign Language (SL), as the mother tongue of the deaf community, is a special visual language that most hearing people cannot understand. In recent years, neural Sign Language Translation (SLT), as a possible way for bridging communication gap between the deaf and the hearing people, has attracted widespread academic attention. We found that the current mainstream end-to-end neural SLT models, which tries to learning language knowledge in a weakly supervised manner, could not mine enough semantic information under the condition of low data resources. Therefore, we propose to introduce additional word-level semantic knowledge of sign language linguistics to assist in improving current end-to-end neural SLT models. Concretely, we propose a novel neural SLT model with multi-modal feature fusion based on the dynamic graph, in which the cross-modal information, i.e. text and video, is first assembled as a dynamic graph according to their correlation, and then the graph is processed by a multi-modal graph encoder to generate the multi-modal embeddings for further usage in the subsequent neural translation models. To the best of our knowledge, we are the first to introduce graph neural networks, for fusing multi-modal information, into neural sign language translation models. Moreover, we conducted experiments on a publicly available popular SLT dataset RWTH-PHOENIX-Weather-2014T. and the quantitative experiments show that our method can improve the model.

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