Modelling the Interpretation of Discourse Connectives by Bayesian Pragmatics

Abstract

marked by discourse connectives (DCs), such as ‘but’, ‘as a result’, or implied implicitly, as in the following examples: We propose a framework to model human comprehension of discourse connectives. Following the Bayesian pragmatic paradigm, we advocate that discourse connectives are interpreted based on a simulation of the production process by the speaker, who, in turn, considers the ease of interpretation for the listener when choosing connectives. Evaluation against the sense annotation of the Penn Discourse Treebank confirms the superiority of the model over literal comprehension. A further experiment demonstrates that the proposed model also improves automatic discourse parsing. 1 1. He came late. In fact, he came at noon. 2. It is late. I will go to bed. The explicit DC ‘in fact’ in Example (1) marks a Specification relation. On the other hand, a Result relation can be inferred between the two sentences in Example (2) although there are not any explicit markers. We say the two sentences (called arguments) are connected by an implicit DC. Discourse relations have a mixture of semantic and pragmatic properties (Van Dijk, 1980; Lewis, 2006). For example, the sense of a discourse relation is encoded in the semantics of a DC (Example (1)), yet the interpretation of polysemic DCs (such as ‘since’, ‘as’) and implicit DCs relies on the pragmatic context (Example (2)). This work seeks to find out if Bayesian pragmatic approaches are applicable to human comprehension of discourse relations. Our contribution includes: (i) an adaptation of the Bayesian Rational Speech Acts model to DC interpretation using a discourse-annotated corpus, the Penn Discourse Treebank; (ii) integration of the proposed model with a state-of-the-art automatic discourse parser to improve discourse sense classification.