Towards Conversation Entailment: An Empirical Investigation

While a significant amount of research has been devoted to textual entailment, automated entailment from conversational scripts has received less attention. To address this limitation, this paper investigates the problem of conversation entailment: automated inference of hypotheses from conversation scripts. We examine two levels of semantic representations: a basic representation based on syntactic parsing from conversation utterances and an augmented representation taking into consideration of conversation structures. For each of these levels, we further explore two ways of capturing long distance relations between language constituents: implicit modeling based on the length of distance and explicit modeling based on actual patterns of relations. Our empirical findings have shown that the augmented representation with conversation structures is important, which achieves the best performance when combined with explicit modeling of long distance relations.