Mohammed Mediani

The Karlsruhe Institute of Technology Systems for the News Translation Task in WMT 2016.

We present our experiments in the scope of the news translation task in WMT 2018, in directions: English→German. The core of our systems is the encoder-decoder based neural machine translation models using the transformer architecture. We enhanced the model with a deeper architecture. By using techniques to limit the memory consumption, we were able to train models that are 4 times larger on one GPU and improve the performance by 1.2 BLEU points. Furthermore, we performed sentence selection for the newly available ParaCrawl corpus. Thereby, we could improve the effectiveness of the corpus by 0.5 BLEU points.

Parallel Phrase Scoring for Extra-large Corpora

Parallel Phrase Scoring for Extra-large Corpora This paper presents a C++ implementation of the phrase scoring step in phrase-based systems that helps to exploit the available computing resources more efficiently and trains very large systems in reasonable time without sacrificing the systems performance in terms of Bleu score. Three parallelizing tools are made freely available. The first exploits shared memory parallelism and multiple disks for parallel IOs while the two others run in a distributed environment. We demonstrate the efficiency and consistency of our tools, in the framework of the Fr-En systems we developed for the WMT and IWSLT evaluation campaigns, in which we were able to generate the phrase table in one third up to one seventh of the time taken by Moses in the same tasks.