Temporal coherence in discourse: Theory and application for machine translation

Abstract

Temporal coherence in discourse is provided through several temporal cohesive ties, such as tense, aspect and discourse connectives. In the relevance theoretic framework and more specifically in the Geneva school of pragmatics, these cohesive ties are considered as encoding procedural information important for guiding the hearer towards the intended interpretation of the discourse. Jacques Moeschler and his team studied temporal cohesive ties and proposed original theoretical models that have been validated with human and automatic annotation experiments, as well as in language acquisition studies (Zufferey and Popescu-Belis, this volume). In this paper, I show that Jacques Moeschler’s model for inferring temporal discourse relations and his description of tenses expressing past time in French is cross-linguistically valid and can be modelled for improving the results of statistical machine translation systems.