SRPOL’s System for the IWSLT 2020 End-to-End Speech Translation Task
Tomasz Potapczyk, Paweł Przybysz · 2020
This paper describes the submission to IWSLT 2020 (Ansari et al., 2020) End-to-End speech translation task by Samsung R&D Institute, Poland.We took part in the offline End-to-End English to German TED lectures translation task.We based our solution on our last year's submission (Potapczyk et al., 2019).We used a slightly altered Transformer( Vaswaniet al., 2017) architecture with ResNet-like(He et al., 2016) convolutional layer preparing the audio input to Transformer encoder.To improve the model's quality of translation we introduced two regularization techniques and trained on machine translated Librispeech(Panayotov et al., 2015) corpus in addition to iwsltcorpus, TEDLIUM2(Rousseau et al., 2014) and Must C(Di Gangi et al., 2019) corpora.Our best model scored almost 3 BLEU higher than last year's model.To segment 2020 test set we used exactly the same procedure as last year.