We describe our experiments on evaluating recently proposed modifications to the discourse relation annotation scheme of the Penn Discourse Treebank (PDTB), in the context of annotating discourse relations in Hindi Discourse Relation Bank (HDRB). While the proposed modifications were driven by the desire to introduce greater conceptual clarity in the PDTB scheme and to facilitate better annotat...

relation de discours, structure du discours discourse relation, discourse structure Résumé-Abstract Dans cet article, nous réexaminons la nature de la relation d'Arrière-Plan de la SDRT du point de vue de la structure du discours. Nous exploitons la méthodologie développée dans (Asher & Vieu, to appear) pour déterminer la nature subordonnante ou coordonnante de cette relation de discours. In th...

It is a widely accepted fact that coherence enables a text’s comprehensibility. A major source of coherence is discourse cohesion (textual properties of the text). Lexical cohesion (e.g. synonymy) and discourse connectives are two major types of discourse cohesion. We investigate the contribution of these two types of cohesion to the overall comprehension of bi-clausal sentences in Turkish. In ...

We describe our initial efforts towards developing a large-scale corpus of Hindi texts annotated with discourse relations. Adopting the lexically grounded approach of the Penn Discourse Treebank (PDTB), we present a preliminary analysis of discourse connectives in a small corpus. We describe how discourse connectives are represented in the sentence-level dependency annotation in Hindi, and disc...

This paper explores the possibilities and limits of a discourse grammar applied to spontaneous speech. Most discourse grammars (e.g. SDRT, Asher, 1993; RST, Mann & Thompson, 1988) tend to be descriptive theories of written discourse which presuppose a coherent structure. This structure is the outcome of a goal directed planning process on the part of the producer. In order to obtain a better un...

Implicit discourse relation recognition is difficult due to the absence of explicit discourse connectives between arbitrary spans of text. In this paper, we use language models to predict the discourse connectives between the arguments pair. We present two methods to apply the predicted connectives to implicit discourse relation recognition. One is to use the sense frequency of the specific con...

Discourse connectives (e.g. however, because) are terms that can explicitly convey a discourse relation within a text. While discourse connectives have been shown to be an effective clue to automatically identify discourse relations, they are not always used to convey such relations, thus they should first be disambiguated between discourse-usage and non-discourse-usage. In this paper, we inves...

Discourse relation classification is an important component for automatic discourse parsing and natural language understanding. The performance bottleneck of a discourse parser comes from implicit discourse relations, whose discourse connectives are not overtly present. Explicit discourse connectives can potentially be exploited to collect more training data to collect more data and boost the p...

Recently, Chinese implicit discourse relation recognition has attracted more and more attention, since it is crucial to understand the Chinese discourse text. In this paper, we propose a novel memory augmented attention model which represents the arguments using an attention-based neural network and preserves the crucial information with an external memory network which captures each discourse ...

Discourse relation recognition is the task of identifying the semantic relationships between textual units. Conventional approaches to discourse relation recognition exploit surface information and syntactic information as machine learning features. However, the performance of these models is severely limited for implicit discourse relation recognition. In this paper, we propose an abductive th...