Sign Language Translation with Transformers
Table 10: SLT performance using STMC for CSLR. The first set of rows correspond to the current state-of-the-arts included for comparison.
['Model', 'Dev Set BLEU-1', 'Dev Set BLEU-2', 'Dev Set BLEU-3', 'Dev Set BLEU-4', 'Dev Set ROUGE-L', 'Dev Set METEOR', 'Test Set BLEU-1', 'Test Set BLEU-2', 'Test Set BLEU-3', 'Test Set BLEU-4', 'Test Set ROUGE-L', 'Test Set METEOR']
To begin, we use the best performing model from the last experiment on German G2T, and we simply feed the output of the STMC network to this model to obtain German translations. What is surprising, however, is that this end-to-end SLT system using STMC and seq2seq networks outperforms the previous G2T result that simulates a system with a perfect CSLR network and a seq2seq model.