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An Improved Sign Language Translation Model with Explainable Adaptations for Processing Long Sign Sentences.

Jiangbin Zheng1, Zheng Zhao2, Min Chen2

  • 1Department of Artificial Intelligence, School of Informatics, Xiamen University, Xiamen 361005, China.

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This study enhances sign language translation (SLT) by optimizing neural models to process longer sign sentences more efficiently. New methods reduce redundant frames and improve feature extraction, boosting translation accuracy.

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Natural Language Processing

Background:

  • Sign language translation (SLT) aims to bridge communication gaps but struggles with long sign sentences due to dependencies and resource needs.
  • Current neural SLT models exhibit limitations in handling complex, lengthy sign sequences effectively.

Purpose of the Study:

  • To propose explainable adaptations for neural SLT models to improve performance on long sign sentences.
  • To enhance the efficiency and accuracy of sign language translation systems.

Main Methods:

  • Introduced a frame stream density compression (FSDC) algorithm to reduce redundant frames in sign language videos.
  • Replaced the traditional NMT encoder with an improved architecture featuring temporal convolution (T-Conv) and dynamic hierarchical bidirectional GRU (DH-BiGRU) units.

Main Results:

  • The proposed model demonstrated superior performance compared to state-of-the-art baselines on the RWTH-PHOENIX-Weather 2014T dataset.
  • Achieved significant improvements, including up to 1.5+ BLEU-4 score gains, indicating enhanced translation quality.

Conclusions:

  • The developed adaptations effectively address the challenges of processing long sign sentences in SLT.
  • The optimized tokenization and improved encoder architecture lead to more accurate and resource-efficient sign language translation.