This paper describes the MeMAD project entry to the IWSLT Speech Translation
Shared Task, addressing the translation of English audio into German text.
Between the pipeline and end-to-end model tracks, we participated only in the
former, with three contrastive systems. We tried also the latter, but were not
able to finish our end-to-end model in time.
All of our systems start by transcribing the audio into text through an
automatic speech recognition (ASR) model trained on the TED-LIUM English Speech
Recognition Corpus (TED-LIUM). Afterwards, we feed the transcripts into
English-German text-based neural machine translation (NMT) models. Our systems
employ three different translation models trained on separate training sets
compiled from the English-German part of the TED Speech Translation Corpus
(TED-Trans) and the OpenSubtitles2018 section of the OPUS collection.
In this paper, we also describe the experiments leading up to our final
systems. Our experiments indicate that using OpenSubtitles2018 in training
significantly improves translation performance. We also experimented with
various pre- and postprocessing routines for the NMT module, but we did not
have much success with these.
Our best-scoring system attains a BLEU score of 16.45 on the test set for
this year's task.