A Corpus-based Contrastive Analysis for Defining Minimal Semantics of Inter-sentential Dependencies for Machine Translation

Inter-sentential dependencies such as discourse connectives or pronouns have an impact on the translation of these items. These dependencies have classically been analyzed within complex theoretical frameworks, often monolingual ones, and the resulting fine-grained descriptions, although relevant to translation, are likely beyond reach of statistical machine translation systems. Instead, we propose an approach to search for a minimal, feature-based characterization of translation divergencies due to inter-sentential dependencies, in the case of discourse connectives and pronouns, based on contrastive analyses performed on the Europarl corpus. In addition, we show how to automatically assign labels to connectives and pronouns, and how to use them for statistical machine translation. 1. The Need for Inter-sentential Information in Machine Translation Long-range dependencies are a well known challenge for machine translation (MT) systems, especially for statistical ones. The correct translation of lexical items such as pronouns often depends on the correct identification of their antecedent. Similarly, the correct translation of multi-functional discourse connectives depends on the correct identification of the rhetorica