Discourse Relations and Connectives in Higher Text Structure

Abstract

The present article investigates possibilities and limits of local (shallow) analysis of discourse coherence with respect to the phenomena of global coherence and higher composition of texts. We study corpora annotated with local discourse relations in Czech and partly in English to try and find clues in the local annotation indicating a higher discourse structure. First, we classify patterns of subsequent or overlapping pairs of local relations, and hierarchies formed by nested local relations. Special attention is then given to relations crossing paragraph boundaries and their semantic types, and to paragraph-initial discourse connectives. In the third part, we examine situations in which annotators incline to marking a large argument (larger than one sentence) of a discourse relation even with a minimality principle annotation rule in place. Our analyses bring (i) new linguistic insights regarding coherence signals in local and higher contexts, e.g. detection and description of hierarchies of local discourse relations up to 5 levels in Czech and English, description of distribution differences in semantic types in cross-paragraph and other settings, identification of Czech connectives only typical for higher structures, or the detection of prevalence of large left-sided arguments in locally annotated data; (ii) as another type of contribution, some new reflections on methodologies of the approaches under scrutiny.

Unsupervised Adversarial Domain Adaptation for Implicit Discourse Relation Classification

“…Resources for implicit discourse relations are scarce compared to the explicit ones, since they are harder to annotate (Miltsakaki et al, 2004). For example, among corpora annotated with discourse relations such as Arabic (Al-Saif and Markert, 2010), Czech (Poláková et al, 2013), Chinese (Zhou and Xue, 2015), English (Prasad et al, 2008), Hindi (Oza et al, 2009), and Turkish (Zeyrek et al, 2013), only the Chinese, English and Hindi corpora include implicit discourse relations (Prasad et al, 2014). In this low-resource scenario, Ji et al (2015) proposed training with explicit relations via unsupervised domain adaptation, viewing explicit relations as a source domain with labeled training data, and implicit relations as a target domain with no labeled data.…”