Discourse parsing is an important research area in natural language processing (NLP),
which aims to parse the discourse structure of coherent sentences. In this survey, we …

In this paper, we show how three often used and seemingly different discourse annotation
frameworks–Penn Discourse Treebank (PDTB), Rhetorical Structure Theory (RST), and …

We present a new lexicon of English discourse connectives called DiMLex-Eng, built by
merging information from two annotated corpora and an additional list of relation signals …

Discourse parsers recognize the intentional and inferential relationships that organize
extended texts. They have had a great influence on a variety of NLP tasks as well as …

The various meanings of discourse connectives like while and however are difficult to
identify and annotate, even for trained human annotators. This problem is all the more …

As the quality of Machine Translation (MT) improves, research on improving discourse in
automatic translations becomes more viable. This has resulted in an increase in the amount …

Current approaches to statistical machine translation assume that sentences in a text are
independent, ignoring the property of connectedness present in virtually all discourse. We …

In this paper, we present a tangible outcome of the TextLink network: a joint online database
project displaying and linking existing and newly-created lexicons of discourse connectives …

Abstract Machine translation (MT) is an important task in natural language processing (NLP)
as it automates the translation process and reduces the reliance on human translators. With …

This paper shows how the disambiguation of discourse connectives can improve their
automatic translation, while preserving the overall performance of statistical MT as …