Discourse structures have a central role in several computational tasks, such as question-answering or dialogue generation. In particular, the framework of the Rhetorical Structure Theory (RST) offers a sound formalism for hierarchical text organization. In this article, we present HILDA, an implemented discourse parser based on RST and Support Vector Machine (SVM) classification. SVM classifiers are trained and applied to discourse segmentation and relation labeling. By combining labeling with a greedy bottom-up tree building approach, we are able to create accurate discourse trees in linear time complexity. Importantly, our parser can parse entire texts, whereas the publicly available parser SPADE (Soricut and Marcu 2003) is limited to sentence level analysis. HILDA outperforms other discourse parsers for tree structure construction and discourse relation labeling. For the discourse parsing task, our system reaches 78.3% of the performance level of human annotators. Compared to a state-of-the-art rule-based discourse parser, our system achieves a performance increase of 11.6%.

In this paper, we explore instance-based learning methods for dialogue act classification on two corpora, MapTask and CallHome Spanish. We start with Latent Semantic Analysis (LSA), and extend it as Feature Latent Semantic Analysis (FLSA). FLSA adds richer linguistic features to LSA, which only uses words. In particular, we explore the extended dialogue context, both linearly (the previous dialogue act) and hierarchically (conversational games). We show how the k-Nearest Neighbor algorithm obtains its best results when applied to the reduced semantic spaces generated by FLSA. Empirically, our results are better than previously published results on these two corpora; linguistically, we confirm and extend previous observations that the hierarchical dialogue structure encoded via the notion of Game is of primary importance for dialogue act recognition.

Collaborative completions are among the strongest evidence that dialogue requires coordination even at the sub-sentential level; the study of sentence completions may thus shed light on a number of central issues both at the ‘macro’ level of dialogue management and at the ‘micro’ level of the semantic interpretation of utterances. We propose a treatment of collaborative completions in PTT, a theory of interpretation in dialogue that provides some of the necessary ingredients for a formal account of completions at the ‘micro’ level, such a theory of incremental utterance interpretation and an account of grounding. We argue that an account of semantic interpretation in completions can be provided through relatively straightforward generalizations of existing theories of syntax such as Lexical Tree Adjoining Grammar (LTAG) and of semantics such as (Compositional) DRT and SituationSemantics. At the macro level, we provide an intentional account of completions, as well as a preliminary account within Pickering and Garrod’s alignment theory.