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An End-to-End Document-Level Neural Discourse Parser Exploiting
  Multi-Granularity Representations

An End-to-End Document-Level Neural Discourse Parser Exploiting Multi-Granularity Representations

21 December 2020
Ke Shi
Zhengyuan Liu
Nancy F. Chen
ArXiv (abs)PDFHTML

Papers citing "An End-to-End Document-Level Neural Discourse Parser Exploiting Multi-Granularity Representations"

4 / 4 papers shown
Can we obtain significant success in RST discourse parsing by using
  Large Language Models?
Can we obtain significant success in RST discourse parsing by using Large Language Models?Conference of the European Chapter of the Association for Computational Linguistics (EACL), 2024
Aru Maekawa
Tsutomu Hirao
Hidetaka Kamigaito
Manabu Okumura
191
8
0
08 Mar 2024
A Simple and Strong Baseline for End-to-End Neural RST-style Discourse
  Parsing
A Simple and Strong Baseline for End-to-End Neural RST-style Discourse ParsingConference on Empirical Methods in Natural Language Processing (EMNLP), 2022
Naoki Kobayashi
Tsutomu Hirao
Hidetaka Kamigaito
Manabu Okumura
Masaaki Nagata
185
12
0
15 Oct 2022
DMRST: A Joint Framework for Document-Level Multilingual RST Discourse
  Segmentation and Parsing
DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing
Zhengyuan Liu
Ke Shi
Nancy F. Chen
177
41
0
09 Oct 2021
Discourse-Aware Neural Extractive Text Summarization
Discourse-Aware Neural Extractive Text SummarizationAnnual Meeting of the Association for Computational Linguistics (ACL), 2019
Jiacheng Xu
Zhe Gan
Yu Cheng
Jingjing Liu
BDL
451
299
0
30 Oct 2019
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