Abstract

One of the important differences between English and French grammar is related to how their verbal systems handle aspectual information. While the English simple past tense is aspectually neutral, the French and Spanish past tenses are linked with a particular imperfective/perfective aspect. This study examines what Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) learn about 'aspect'and how this is reflected in the translations they produce. We use their main knowledge sources, phrase-tables (SMT) and encoding vectors (NMT), to examine what kind of aspectual information they encode. Furthermore, we examine whether this encoded 'knowledge'is actually transferred during decoding and thus reflected in the actual translations. Our study is based on the translations of the English simple past and present perfect tenses into French and Spanish …