Despite recent advances in Natural Language Processing (NLP), hierarchical discourse
parsing in the framework of Rhetorical Structure Theory remains challenging, and our …

While large language models have significantly enhanced the effectiveness of discourse
relation classifications, it remains unclear whether their comprehension is faithful and …

We consider an unanswered question in the discourse processing community: why do
relation classifiers trained on explicit examples (with connectives removed) perform poorly in …

This study introduces an approach for evaluating the importance of signals proposed by Das
and Taboada in discourse parsing. Previous studies using other signals indicate that …

In this article we present Enhanced Rhetorical Structure Theory (eRST), a new theoretical
framework for computational discourse analysis, based on an expansion of Rhetorical …

In this work, we introduce a lightweight discourse connective detection system. Employing
gradient boosting trained on straightforward, low-complexity features, this proposed …

We present a pipeline for multi-lingual Shallow Discourse Parsing. The pipeline exploits
Machine Translation and Word Alignment, by translating any incoming non-English input …

Despite attempts to make Large Language Models multi-lingual, many of the world's
languages are still severely under-resourced. This widens the performance gap between …

Classifying discourse relations is a hard task: discourse-annotated data is scarce, especially
for languages other than English, and there exist different theoretical frameworks that affect …

Discourse segmentation received increased attention in the past years, however the majority
of studies have focused on written genres and with high-resource languages. This paper …