Sign Language Translation with Transformers
Table 4: G2T performance comparison using different embedding schemes on RWTH-PHOENIX-Weather 2014T.
['WE', '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']
It may be because pre-trained embeddings are shown to be more effective when used on the encoding layer [pretrain], but we have no available pre-trained embeddings in German sign language glosses. Another possible reason is the difference between the domain of our dataset and of the corpus the embeddings were trained on, as our dataset has a specific domain of weather forecasts. We therefore keep random initialization of word embeddings for the rest of our experiments on RWTH-PHOENIX-Weather 2014T. Using this setting, we run a parameter search over the learning rate and warm-up steps, and we use initial learning rate 0.5 with 3,000 warm-up steps for the remaining experiments. Details of the parameter search are included as an appendix. We also performed an additional experiment using fastText pre-trained embed-dings and embeddings tying on the decoder, but it does not surpass tied embeddings without pre-trained embeddings. For the remaining experiments, we used tied decoder embeddings with an initial learning rate of 0.2 and 8,000 warm-up steps which are the optimal parameters empirically.