The RWTH Aachen University Filtering System for the WMT 2018 Parallel Corpus Filtering Task
Nick Rossenbach, Jan Rosendahl, Yunsu Kim, Miguel Graça, Aman Gokrani, Hermann Ney · 2018
This paper describes the submission of RWTH Aachen University for the De→En parallel corpus filtering task of the EMNLP 2018 Third Conference on Machine Translation (WMT 2018).We use several rule-based, heuristic methods to preselect sentence pairs.These sentence pairs are scored with count-based and neural systems as language and translation models.In addition to single sentence-pair scoring, we further implement a simple redundancy removing heuristic.Our best performing corpus filtering system relies on recurrent neural language models and translation models based on the transformer architecture.A model trained on 10M randomly sampled tokens reaches a performance of 9.2% BLEU on newstest2018.Using our filtering and ranking techniques we achieve 34.8% BLEU.