An Empirical Analysis of Source Context Features for Phrase-Based Statistical Machine Translation

Statistical phrase-based machine translation systems make only little use of context information: while the language model takes into account target side context, context information on the source side is typically not integrated into phrase-based translation systems. Translational features such as phrase translation probabilities are learned from phrase-translation pairs extracted from word-aligned parallel corpora. Since there is no information besides the co-occurrence frequencies of the phrase-translation pairs, all occurrences of a given source phrase are used for the estimation of translation probabilities, regardless of their contexts in the training data. However, information about the context of a source phrase, e.g. adjacent words or part-of-speech tags, might be a valuable resource for the identification of appropriate translations in a given context. In this work, we want to analyze the use of source side context features in phrase-based statistical machine translation. For every phrase in an input sentence, context-sensitive phrase translation probabilities will be estimated: by reducing the set of all phrase-translation pairs to the subset of those with the same context as the given phrase, we can compute individual translation probabilities depending on the respective context. Assuming that the different translations of ambiguous source phrases occur within different contexts, contextually conditioned translation probabilities might help to solve ambiguities by separating the entire set of translation candidates into subsets appropriate for different situations. However, the more refined probability estimates should also have a general positive influence on translation quality. Furthermore, the integration of context features offers the possibility to include linguistic information which is not used in standard statistical machine translation. In our experiments, which are conducted on an English to German translation system, we will focus on the integration of local context features, choosing a simple method for the computation of contextually conditioned phrase-translation probabilities and their incorporation into a standard phrase-based statistical translation system. For all experiments, we will provide an extensive evaluation of the overall translation quality using standard automatic metrics such as bleu, but also attempt to individually rate fluency and adequacy.