Discourse Understanding is hard. This seems to be especially true for mathematical discourse, that is proofs. Restricting discourse to mathematical discourse allow us, however, to study the subject matter in its purest form. This domain of discourse is rich and welldefined, highly structured, offers a well-defined set of discourse relations and forces/allows us to apply mathematical reasoning. ...

The corpora available for training discourse relation classifiers are annotated using a general set of discourse relations. However, for certain applications, custom discourse relations are required. Creating a new annotated corpus with a new relation taxonomy is a timeconsuming and costly process. We address this problem by proposing a semi-supervised approach to discourse relation classificat...

I In this paper we present algorithms for the interpretation and generation of a kind of particu-larized conversational implicature occurring in certain indirect replies. Our algorithms make use of discourse expectations, discourse plans, and discourse relations. The algorithms calculate implica-tures of discourse units of one or more sentences. Our approach has several advantages. First, by ta...

Previous work by Lin et al. (2011) demonstrated the effectiveness of using discourse relations for evaluating text coherence. However, their work was based on discourse relations annotated in accordance with the Penn Discourse Treebank (PDTB) (Prasad et al., 2008), which encodes only very shallow discourse structures; therefore, they cannot capture long-distance discourse dependencies. In this ...

A spontaneously spoken, natural Japanese discourse contains many instances of the so-called redundant interjections and of back-channel utterances. These expressions have not hitherto received much attention and few systematic analyses have been made, since they were regarded as useless, spurious expressions. On the basis of the analysis of spoken dialogue corpus, we claim that these utterances...

A natural and intuitive principle concerning the organization of content in discourse is that discourse structure and rhetorical function operate at several levels of granularity at once. There are low level discourse connections between elementary discourse units (EDUs), even within a single sentence; but there are also discourse connections between larger constituents, complex discourse units...

Discourse markers, also known as clue words, are used extensively in human-human task-oriented dialogs to signal the structure of the discourse. Previous work showed their importance in monologs and social conversations for marking discourse structure , but little attention has been paid to their importance in spoken dialog systems. This paper investigates what discourse markers signal about th...

The Penn Discourse Treebank (PDTB) was released to the public in 2008 and remains the largest corpus of manually annotated discourse relations — both relations that are signaled explicitly (e.g., by a coordinating or subordinating conjunction, or by a discourse adverbial or other construction) and ones that otherwise appear implicit. The Penn Discourse TreeBank also diverges from other discours...

Computational approaches to discourse understanding have a two-part goal: (1) to identify those aspects of discourse understanding that require process-based accounts, and (2)to characterize the processes and data structures they involve. To date, in the area of reference, process-based accounts have been developed for subsequent reference via anaphoric pronouns and reference via definite descr...

The explicit consideration of an addressee’s inferences during discourse planning affects the content and coherence of the generated discourse. The content of the discourse is affected because the consideration of an addressee’s inferences may require the addition of information that addresses erroneous inferences or suggest the omission of easily inferred information. The coherence of the disc...