Pronominal Anaphora in Machine Translation

State-of-the-art machine translation systems use strong assumptions of independence. Following these assumptions language is split into small segments such as sentences and phrases which are translated independently. Natural language, however, is not independent: many concepts depend on context. One such case is reference introduced by pronominal anaphora. In pronominal anaphora a pronoun word (anaphor) refers to a concept mentioned earlier in the text (antecedent). This type of reference can refer to something in the same sentence, but it can also span many sentences. Pronominal anaphora pose a challenge for translators since the anaphor has to fulfil some grammatical agreement with the antecedent. This means that the reference has to be detected in the source text before translation and the translator needs to ensure that this reference still holds true in the translation. The independence assumptions of current machine translation systems do not allow for this. We study pronominal anaphora in two tasks of English–German machine translation. We analyse occurrence of pronominal anaphora and their current translation performance. In this analysis we find that the implicit handling of pronominal anaphora in our baseline translation system is not sufficient. Therefore we develop four approaches to handle pronominal anaphora explicitly. Two of these approaches are based on post-processing. In the first one we correct pronouns directly and in the second one we select a hypothesis with correct pronouns from the translation system’s n-best list. Both of these approaches improve the translation accuracy of the pronouns but hardly change the translation quality measured in BLEU. The other two approaches predict translations of pronoun words and can be used in the decoder. The Discriminative Word Lexicon (DWL) predicts the probability of a target word to be used in the translation and the Source DWL (SDWL) directly predicts the translation of a source language pronoun. However, these predictions do not improve the quality already achieved by the translation system.