Empirical Studies In Discourse

Computational theories of discourse are concerned with the context-based interpretation or generation of discourse phenomena in text and dialogue. In the past, research in this area focused on specifying the mechanisms underlying particular discourse phenomena; the models proposed were often motivated by a few constructed examples. While this approach led to many theoretical advances, models developed in this manner are difficult to evaluate because it is hard to tell whether they generalize beyond the particular examples used to motivate them. Recently however the field has turned to issues of robustness and the coverage of theories of particular phenomena with respect to specific types of data. This new empirical focus is supported by several recent advances: an increasing theoretical consensus on discourse models; a large amount of on-line dialogue and textual corpora available; and improvements in component technologies and tools for building and testing discourse and dialogue testbeds. This means that it is now possible to determine how representative particular discourse phenomena are, how frequently they occur, whether they are related to other phenomena, what percentage of the cases a particular model covers, the inherent difficulty of the problem, and how well an algorithm for processing or generating the phenomena should perform to be considered a good model. This issue brings together a collection of papers illustrating recent approaches to empirical research in discourse generation and interpretation. Section 2 gives a general overview of empirical studies in discourse and describes an empirical research strategy that leads from empirical findings to general theories. Section 3 discusses how each article exemplifies the empirical research strategy and how empirical methods have been employed in each research project.