In order to minimize the generalization error in neural networks, a novel technique to identify
overfitting phenomena when training the learner is formally introduced. This enables support …

In this paper, we present a Linguistic Informed Multi-Task BERT (LIMIT-BERT) for learning
language representations across multiple linguistic tasks by Multi-Task Learning (MTL) …

Constituency parsing and nested named entity recognition (NER) are similar tasks since
they both aim to predict a collection of nested and non-crossing spans. In this work, we cast …

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

Understanding discourse structures of news articles is vital to effectively contextualize the
occurrence of a news event. To enable computational modeling of news structures, we apply …

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 …

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 …

We introduce a top-down approach to discourse parsing that is conceptually simpler than its
predecessors (Kobayashi et al., 2020; Zhang et al., 2020). By framing the task as a …

Previous studies have made great advances in RST discourse parsing through specific
neural frameworks or features, but they usually split the parsing process into two subtasks …

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 …