The paper describes the 3 NMT models submitted by the eTranslation team to the WMT 2021 news translation shared task. We developed systems in language pairs that are actively used in the European Commission’s eTranslation service. In the WMT news task, recent years have seen a steady increase in the need for computational resources to train deep and complex architectures to produce competitive systems. We took a different approach and explored alternative strategies focusing on data selection and filtering to improve the performance of baseline systems. In the domain constrained task for the French–German language pair our approach resulted in the best system by a significant margin in BLEU. For the other two systems (English–German and English-Czech) we tried to build competitive models using standard best practices.

We propose a bootstrapping approach to creating a phrase-level alignment over a sentence-aligned parallel corpus, reporting concrete treebank annotation work performed on a sample of sentence tuples from the Europarl corpus, currently for English, French, German, and Spanish. The manually annotated seed data will be used as the basis for automatically labelling the rest of the corpus. Some preliminary experiments addressing the bootstrapping aspects are presented. The representation format for syntactic correspondence across parallel text that we propose as the starting point for a process of successive refinement emphasizes correspondences of major constituents that realize semantic arguments or modifiers; language-particular details of morphosyntactic realization are intentionally left largely unlabelled. We believe this format is a good basis for training NLPtools for multilingual application contexts in which consistency across languages is more central than fine-grained details in specific languages (in particular, syntax-based statistical Machine Translation).