The impact of some linguistic features on the quality of neural machine translation

This paper investigates how different features influence the translation quality of a Russian-English neural machine translation system. All the trained translation models are based on the OpenNMT-py system and share the state-of-the-art Transformer architecture. The majority of the models use the Yandex English-Russian parallel corpus as training data. The BLEU score on the test data of the WMT18 news translation task is used as the main measure of performance. In total, five different features are tested: tokenization, lowercase, the use of BPE (byte-pair encoding), the source of BPE, and the training corpus. The study shows that the use of tokenization and BPE seems to give considerable advantage while lowercase impacts the result insignificantly. As to the BPE vocabulary source, the use of bigger monolingual corpora such as News Crawl as opposed to the training corpus may provide a greater advantage. The thematic correspondence of the training and test data proved to be crucial. Quite high scores of the models so far may be attributed to the fact that both the Yandex parallel corpus and the WMT18 test set consist largely of news texts. At the same time, the models trained on the Open Subtitles parallel corpus show a substantially lower score on the WMT18 test set, and one comparable to the other models on a subset of Open Subtitles corpus not used in training. The expert evaluation of the two highest-scoring models showed that neither excels current Google Translate. The paper also provides an error classification, the most common errors being the wrong translation of proper names and polysemantic words