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Found 7 papers, 4 papers with code
EDTC: A Corpus for Discourse-Level Topic Chain Parsing
Discourse analysis has long been known to be fundamental in natural language processing.
Coupling Context Modeling with Zero Pronoun Recovering for Document-Level Natural Language Generation
Natural language generation (NLG) tasks on pro-drop languages are known to suffer from zero pronoun (ZP) problems, and the problems remain challenging due to the scarcity of ZP-annotated NLG corpora.
Discourse Cohesion Evaluation for Document-Level Neural Machine Translation
It is well known that translations generated by an excellent document-level neural machine translation (NMT) model are consistent and coherent.
Adversarial Learning for Discourse Rhetorical Structure Parsing
Text-level discourse rhetorical structure (DRS) parsing is known to be challenging due to the notorious lack of training data.
Coreference Resolution: Are the eliminated spans totally worthless?
Various neural-based methods have been proposed so far for joint mention detection and coreference resolution.
A Top-Down Neural Architecture towards Text-Level Parsing of Discourse Rhetorical Structure
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 drawing more and more attention in recent years.
Hierarchical Modeling of Global Context for Document-Level Neural Machine Translation
In this paper, we propose a hierarchical model to learn the global context for document-level neural machine translation (NMT).
