Discourse structures are beneficial for various NLP tasks such as dialogue understanding,
question answering, sentiment analysis, and so on. This paper presents a deep sequential …

Abstract Syntax has been a useful source of information for statistical RST discourse
parsing. Under the neural setting, a common approach integrates syntax by a recursive …

Pre-trained language models (PLMs) have shown great potentials in natural language
processing (NLP) including rhetorical structure theory (RST) discourse parsing. Current …

We propose an efficient neural framework for sentence-level discourse analysis in
accordance with Rhetorical Structure Theory (RST). Our framework comprises a discourse …

Text-level discourse rhetorical structure (DRS) parsing is known to be challenging due to the
notorious lack of training data. Although recent top-down DRS parsers can better leverage …

Using style-transfer models to reduce offensiveness of social media comments can help
foster a more inclusive environment. However, there are no sizable datasets that contain …

We consider the end-to-end abstract-to-title generation problem, exploring seven recent
transformer based models (including ChatGPT) fine-tuned on more than 30k abstract-title …

Some downstream NLP tasks exploit discourse dependency trees converted from RST trees.
To obtain better discourse dependency trees, we need to improve the accuracy of RST trees …

This paper describes our submission to the DISRPT2021 Shared Task on Discourse Unit
Segmentation, Connective Detection, and Relation Classification. Our system, called …

Due to its great importance in deep natural language understanding and various down-
stream applications, text-level parsing of discourse rhetorical structure (DRS) has been …