Discourse connective detection in spoken conversations

Discourse parsing is an important task in Language Understanding with applications to human-human and human-machine communication modeling. However, most of the research has focused on written text, and parsers heavily rely on syntactic parsers that themselves have low performance on dialog data. In our work, we address the problem of analyzing the semantic relations between discourse units in human-human spoken conversations. In particular, in this paper we focus on the detection of discourse connectives which are the predicate of such relations. The discourse relations are drawn from the Penn Discourse Treebank annotation model and adapted to a domain-specific Italian human-human spoken conversations. We study the relevance of lexical and acoustic context in predicting discourse connectives. We observe that both lexical and acoustic context have mixed effect on the prediction of specific connectives. While the oracle of using lexical and acoustic contextual feature combinations is F1 = 68.53, the lexical context alone significantly outperforms the baseline by more than 10 points with F1 = 64.93.