Jointly Trained Transformers models for Spoken Language Translation
Table 4: Transformer based End-to-End SLT.
['Architecture\xa0(Target unit)\xa0(No.of parameters)', 'Dev set', 'Test set']
End-to-End SLT systems are trained using transformer models. The architectural details of the model are described below: the model has 12-encoders, 6 decoder layers with dmodel, dff are 256 and 2048 with 4 heads. The models are trained to predict the Portuguese characters/BPE units. The models are trained for 150 epochs and each batch comprised of 7000 target units. The models form the last ten epochs are averaged and the averaged weight is used for decoding the hypothesis. The hypotheses are decoded with a beam of 10 and length penalty of 1. The performance of the End-to-End SP-MT are tabulated in Table. From the Table. The performance of these models is worse than the performance of the cascaded SLT models. To increase the model capacity, additional layers a have been added to encoder and decoder and the architecture of the model is in column 1. Row 8 of Table.