The aim of this paper is twofold: it illustrates the benefits of rule-based instead of statistical machine translation, and it provides a starting point for the machine translation of the Russian aspect into English. Rule-based machine translation is still promising, from both a computational and theoretical point of view, because by implementing rules on the computer theoretical assumptions concerning linguistic structures can be verified and improved.
This will be shown using the example of the category of aspect, which is one of the main challenges for machine translation from Russian to English. A small corpus study on the translation of Russian sentences with verbs in the past tense (perfective and imperfective) by human translators shows that three-quarters of Russian verbs (both imperfective and perfective) are translated by English simple past forms. While this results from language internal markedness relations, the translation of the remaining 25 % requires an in-depth analysis of the various interpretations possible for the Russian aspect. We propose a semantic analysis based on which rules for the interpretation and translation of Russian aspect in a machine translation system can be derived. Their implementation in the machine translation system ETAP is shown in this paper using two test cases as examples.