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In this paper, we investigate the use of transformers for Neural MachineTranslation of text-to-GLOSS for Deaf and Hard-of-Hearing communication. Due tothe scarcity of available data and limited resources for text-to-GLOSStranslation, we treat the problem as a low-resource language task. We use ournovel hyper-parameter exploration technique to explore a variety ofarchitectural parameters and build an optimal transformer-based architecturespecifically tailored for text-to-GLOSS translation. The study aims to improvethe accuracy and fluency of Neural Machine Translation generated GLOSS. This isachieved by examining various architectural parameters including layer count,attention heads, embedding dimension, dropout, and label smoothing to identifythe optimal architecture for improving text-to-GLOSS translation performance.The experiments conducted on the PHOENIX14T dataset reveal that the optimaltransformer architecture outperforms previous work on the same dataset. Thebest model reaches a ROUGE (Recall-Oriented Understudy for Gisting Evaluation)score of 55.18% and a BLEU-1 (BiLingual Evaluation Understudy 1) score of63.6%, outperforming state-of-the-art results on the BLEU1 and ROUGE score by8.42 and 0.63 respectively.

Advancing Text-to-GLOSS Neural Translation Using a Novel Hyper-parameter Optimization Technique