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Cross-Paragraph Discourse Structure ...

Peng, Siyao.

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Cross-Paragraph Discourse Structure in Rhetorical Structure Theory Parsing and Treebanking for Chinese and English.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Cross-Paragraph Discourse Structure in Rhetorical Structure Theory Parsing and Treebanking for Chinese and English./
Author:	Peng, Siyao.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	168 p.
Notes:	Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
Contained By:	Dissertations Abstracts International84-11B.
Subject:	Linguistics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30419618
ISBN:	9798379454388

Cross-Paragraph Discourse Structure in Rhetorical Structure Theory Parsing and Treebanking for Chinese and English.
Peng, Siyao.

Cross-Paragraph Discourse Structure in Rhetorical Structure Theory Parsing and Treebanking for Chinese and English. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 168 p.

Source: Dissertations Abstracts International, Volume: 84-11, Section: B.

Thesis (Ph.D.)--Georgetown University, 2023.

This item must not be sold to any third party vendors.

Hierarchical discourse structures benefit Natural Language Understanding tasks, such as text summarization and sentiment analysis. Rhetorical Structure Theory (RST) is particularly significant at the macro levels, such as between paragraphs. Moreover, RST parsing at the macro level is more challenging than at the micro level, where intra-sentential relations are the easiest to identify.Despite a dozen RST datasets available in multiple languages, a sizeable Chinese RST corpus still needs to be created. Moreover, there awaits an in-depth analysis regarding how much RST associates with macro-level structures and how much parsing performance deteriorates at the macro level and across genres. Using English and Chinese as examples, this dissertation examines how macro-level discourse relations are presented in RST and whether state-of-the-art RST parsers capture them properly.{A0}Firstly, I create the largest Chinese RST Corpus, namely Georgetown Chinese Discourse Treebank (GCDT), an open-source treebank with 50 medium-to-long documents from five different genres. I present basic statistics and highlight annotation decisions for Mandarin Chinese. I believe this sizeable multi-genre RST corpus can promote discourse analysis and RST parsing in Chinese and across languages.Secondly, I examine the association between paragraphs and RST trees from three aspects: a) studying how the lengths of EDU, sentence, and paragraph segments differ{A0}across genres and corpora; b) assessing whether or not paragraphs are fully contained in RST subtrees and 3) analyzing the distribution of intra- versus inter-paragraph relations across corpora and genres.Thirdly, I conduct parsing experiments on Chinese GCDT and English GUM using a state-of-the-art multilingual RST parser. I present both datasets' benchmark monolingual and multilingual parsing scores and boost the performance by pretraining and automatic translation. Moreover, I show that SOTA parsers are unsatisfactory in some genres and the inter-paragraph scenario.{A0}

ISBN: 9798379454388Subjects--Topical Terms:

524476
Linguistics.
Subjects--Index Terms:

Chinese

Cross-Paragraph Discourse Structure in Rhetorical Structure Theory Parsing and Treebanking for Chinese and English.
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300 $a 168 p.
500 $a Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
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502 $a Thesis (Ph.D.)--Georgetown University, 2023.
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520 $a Hierarchical discourse structures benefit Natural Language Understanding tasks, such as text summarization and sentiment analysis. Rhetorical Structure Theory (RST) is particularly significant at the macro levels, such as between paragraphs. Moreover, RST parsing at the macro level is more challenging than at the micro level, where intra-sentential relations are the easiest to identify.Despite a dozen RST datasets available in multiple languages, a sizeable Chinese RST corpus still needs to be created. Moreover, there awaits an in-depth analysis regarding how much RST associates with macro-level structures and how much parsing performance deteriorates at the macro level and across genres. Using English and Chinese as examples, this dissertation examines how macro-level discourse relations are presented in RST and whether state-of-the-art RST parsers capture them properly.{A0}Firstly, I create the largest Chinese RST Corpus, namely Georgetown Chinese Discourse Treebank (GCDT), an open-source treebank with 50 medium-to-long documents from five different genres. I present basic statistics and highlight annotation decisions for Mandarin Chinese. I believe this sizeable multi-genre RST corpus can promote discourse analysis and RST parsing in Chinese and across languages.Secondly, I examine the association between paragraphs and RST trees from three aspects: a) studying how the lengths of EDU, sentence, and paragraph segments differ{A0}across genres and corpora; b) assessing whether or not paragraphs are fully contained in RST subtrees and 3) analyzing the distribution of intra- versus inter-paragraph relations across corpora and genres.Thirdly, I conduct parsing experiments on Chinese GCDT and English GUM using a state-of-the-art multilingual RST parser. I present both datasets' benchmark monolingual and multilingual parsing scores and boost the performance by pretraining and automatic translation. Moreover, I show that SOTA parsers are unsatisfactory in some genres and the inter-paragraph scenario.{A0}
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