Deep learning approaches have achieved great success in the field of Natural Language
Processing (NLP). However, directly training deep neural models often suffer from overfitting …

Multi-task learning (MTL) in deep neural networks for NLP has recently received increasing
interest due to some compelling benefits, including its potential to efficiently regularize …

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 …

Multitask learning has been applied successfully to a range of tasks, mostly
morphosyntactic. However, little is known on when MTL works and whether there are data …

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 …

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 …

This article evaluates purported progress over the past years in RST discourse parsing.
Several studies report a relative error reduction of 24 to 51% on all metrics that authors …

We investigate whether and where multi-task learning (MTL) can improve performance on
NLP problems related to argumentation mining (AM), in particular argument component …

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 …