This paper considers the problem of automatic assessment of local coherence. We present a novel entity-based representation of discourse which is inspired by Centering Theory and can be computed automatically from raw text. We view coherence assessment as a ranking learning problem and show that the proposed discourse representation supports the effective learning of a ranking function. Our experiments demonstrate that the induced model achieves significantly higher accuracy than a state-of-the-art coherence model. 1

Ordering information is a critical task for natural language generation applications. In this

A corpus of German newspaper commentaries has been assembled and annotated with different information (and currently, to different degrees): part-of-speech, syntax, rhetorical structure, connectives, co-reference, and information structure. The paper explains the design decisions taken in the annotations, and describes a number of applications using this corpus with its multi-layer annotation. 1

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In this paper we propose a data intensive approach for inferring sentence-internal temporal relations, which relies on a simple probabilistic model and assumes no manual coding.

Being able to identify which rhetorical relations (e.g., contrast or explanation) hold between spans of text is important for many natural language processing applications. Using machine learning to obtain a classifier which can distinguish between different relations typically depends on the availability of manually labelled training data, which is very time-consuming to create. However, rhetorical relations are sometimes lexically marked, i.e., signalled by discourse markers (e.g., because, but, consequently etc.), and it has been suggested (Marcu and Echihabi, 2002) that the presence of these cues in some examples can be exploited to label them automatically with the corresponding relation. The discourse markers are then removed and the automatically labelled data are used to train a classifier to determine relations even when no discourse marker is present (based on other linguistic cues such as word co-occurrences). In this paper, we investigate empirically how feasible this approach is. In particular, we test whether automatically labelled, lexically marked examples are really suitable training material for classifiers that are then applied to unmarked examples. Our results suggest that training on this type of data may not be such a good strategy, as models trained in this way do not seem to generalise very well to unmarked data. Furthermore, we found some evidence that this behaviour is largely independent of the classifiers used and seems to lie in the data itself (e.g., marked and unmarked examples may be too dissimilar linguistically and removing unambiguous markers in the automatic labelling process may lead to a meaning shift in the examples). 1

We do two things in this paper. First, we present a model of possible causes for requesting clarifications in dialogue, i.e., we classify types of non-understandings that lead to clarifications. For this we

In this paper, we present research toward ontology-based understanding of discourse in meetings and describe an ontology of multimodal discourse designed for this purpose. We investigate its application in an integrated but modular architecture which uses semantically annotated knowledge of communicative meeting activity as well as discourse subject matter. We highlight how this approach assists in improving system performance over time and supports understanding in a changing and persistent environment. We also describe current and future plans for ontology-driven robust naturallanguage understanding in the presence of the highly ambiguous and errorful input typical of the meeting domain.

An approach to dialogue based interaction for resolution of ambiguities encountered as part of Human-Augmented Mapping (HAM) is presented. The paper focuses on issues related to spatial organisation and localisation. The dialogue pattern naturally arises as robots are introduced to novel environments. The paper discusses an approach based on the notion of Questions under Discussion (QUD). The presented approach has been implemented on a mobile platform that has dialogue capabilities and methods for metric SLAM. Experimental results from a pilot study clearly demonstrate that the system can resolve problematic situations. Keywords Human-augmented mapping; natural language dialogue; mixed initiative; clarification 1.

We describe a data-driven approach to building interpretable discourse structures for appointment scheduling dialogues. We represent discourse structures as headed trees and model them with probabilistic head-driven parsing techniques. We show that dialogue-based features regarding turn-taking and domain specific goals have a large positive impact on performance.