1 code implementation • EMNLP 2021 • Xin Tan, Longyin Zhang, Guodong Zhou
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.
1 code implementation • Findings (EMNLP) 2021 • Longyin Zhang, Xin Tan, Fang Kong, Guodong Zhou
Discourse analysis has long been known to be fundamental in natural language processing.
no code implementations • 19 Aug 2022 • Xin Tan, Longyin Zhang, Guodong Zhou
It is well known that translations generated by an excellent document-level neural machine translation (NMT) model are consistent and coherent.
1 code implementation • ACL 2021 • Longyin Zhang, Fang Kong, Guodong Zhou
Text-level discourse rhetorical structure (DRS) parsing is known to be challenging due to the notorious lack of training data.
no code implementations • 4 Jan 2021 • Xin Tan, Longyin Zhang, Guodong Zhou
Various neural-based methods have been proposed so far for joint mention detection and coreference resolution.
1 code implementation • ACL 2020 • Longyin Zhang, Yuqing Xing, Fang Kong, Peifeng Li, Guodong Zhou
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.
no code implementations • IJCNLP 2019 • Xin Tan, Longyin Zhang, Deyi Xiong, Guodong Zhou
In this paper, we propose a hierarchical model to learn the global context for document-level neural machine translation (NMT).