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

TL;DR

This paper introduces a novel hyper-parameter optimization method to enhance transformer-based neural machine translation for text-to-GLOSS, significantly improving translation accuracy for low-resource deaf communication data.

Contribution

The study presents a new hyper-parameter exploration technique that tailors transformer architectures for low-resource text-to-GLOSS translation, achieving state-of-the-art results.

Findings

Optimal transformer architecture outperforms previous models.

Achieved ROUGE score of 55.18% and BLEU-1 score of 63.6%.

Outperforms previous results on the PHOENIX14T dataset.

Abstract

In this paper, we investigate the use of transformers for Neural Machine Translation of text-to-GLOSS for Deaf and Hard-of-Hearing communication. Due to the scarcity of available data and limited resources for text-to-GLOSS translation, we treat the problem as a low-resource language task. We use our novel hyper-parameter exploration technique to explore a variety of architectural parameters and build an optimal transformer-based architecture specifically tailored for text-to-GLOSS translation. The study aims to improve the accuracy and fluency of Neural Machine Translation generated GLOSS. This is achieved by examining various architectural parameters including layer count, attention heads, embedding dimension, dropout, and label smoothing to identify the optimal architecture for improving text-to-GLOSS translation performance. The experiments conducted on the PHOENIX14T dataset reveal that the optimal transformer architecture outperforms previous work on the same dataset. The best model reaches a ROUGE (Recall-Oriented Understudy for Gisting Evaluation) score of 55.18% and a BLEU-1 (BiLingual Evaluation Understudy 1) score of 63.6%, outperforming state-of-the-art results on the BLEU1 and ROUGE score by 8.42 and 0.63 respectively.