ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences

Gao, Yanjun; Huang, Ting-Hao; Passonneau, Rebecca J.

doi:10.18653/v1/2021.acl-long.303

Cited by 2 publications

(4 citation statements)

References 26 publications

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“…The simple sentence decomposition (SSD) task [10] converts complex sentences into a covering set of simple sentences derived from the tensed clauses in the source sentence, where shared nouns or pronouns are copied and discourse connectives (e.g., and, but, although, etc.) are dropped.…”

Section: Simple-sentence-decompositionmentioning

confidence: 99%

“…The intuition to address this issue is first to decompose complex sentences and then re-link simple ones, which share similar ideas with tasks such as rhetorical-structure-theory (RST)-style discourse parsing (RST parsing) [7], split-and-rephrase (SPRP) [8], text simplification (TS) [9], simple sentence decomposition (SSD) (Gao et al. have yet to name their task, and thus, we summarize their main idea into simple-sentence-decomposition (SSD) for the convenience of writing) [10], etc.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical Clause Annotation: Building a Clause-Level Corpus for Semantic Parsing with Complex Sentences

Fan,

Li,

Sataer

et al. 2023

Applied Sciences

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Most natural-language-processing (NLP) tasks suffer performance degradation when encountering long complex sentences, such as semantic parsing, syntactic parsing, machine translation, and text summarization. Previous works addressed the issue with the intuition of decomposing complex sentences and linking simple ones, such as rhetorical-structure-theory (RST)-style discourse parsing, split-and-rephrase (SPRP), text simplification (TS), simple sentence decomposition (SSD), etc. However, these works are not applicable for semantic parsing such as abstract meaning representation (AMR) parsing and semantic dependency parsing due to misalignments with semantic relations and unavailabilities to preserve the original semantics. Following the same intuition and avoiding the deficiencies of previous works, we propose a novel framework, hierarchical clause annotation (HCA), for capturing clausal structures of complex sentences, based on the linguistic research of clause hierarchy. With the HCA framework, we annotated a large HCA corpus to explore the potentialities of integrating HCA structural features into semantic parsing with complex sentences. Moreover, we decomposed HCA into two subtasks, i.e., clause segmentation and clause parsing, and provide neural baseline models for more-silver annotations. In evaluating the proposed models on our manually annotated HCA dataset, the performances of clause segmentation and parsing resulted in 91.3% F1-scores and 88.5% Parseval scores, respectively. Due to the same model architectures employed, the performance differences of the clause/discourse segmentation and parsing subtasks was reflected in our HCA corpus and compared discourse corpora, where our sentences contained more segment units and fewer interrelations than those in the compared corpora.

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Section: Simple-sentence-decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hierarchical Clause Annotation: Building a Clause-Level Corpus for Semantic Parsing with Complex Sentences

Fan,

Li,

Sataer

et al. 2023

Applied Sciences

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“…The main work we compare against is WIKISPLIT, a corpus created by extracting split sentences from Wikipedia edit histories (Botha et al, 2018). Concurrent work used a subset of WIKISPLIT to focus on sentence decomposition (Gao et al, 2021). While this approach is able to both extract many potential sentence splits and transfer across languages, edited sentences do not necessarily have to retain the same meaning.…”

Section: Related Workmentioning

confidence: 99%

“…We instead start with a BERT-initialized transformer model (Rothe et al, 2020), and train it with an adaptive loss function to emphasize split-based edits. Concurrent work also introduced an additional neural graph-approach for Split and Rephrase (Gao et al, 2021).…”

Section: Related Workmentioning

confidence: 99%

BiSECT: Learning to Split and Rephrase Sentences with Bitexts

Kim¹,

Maddela²,

Kriz³

et al. 2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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An important task in NLP applications such as sentence simplification is the ability to take a long, complex sentence and split it into shorter sentences, rephrasing as necessary. We introduce a novel dataset and a new model for this 'split and rephrase' task. Our BISECT training data consists of 1 million long English sentences paired with shorter, meaning-equivalent English sentences. We obtain these by extracting 1-2 sentence alignments in bilingual parallel corpora and then using machine translation to convert both sides of the corpus into the same language. BISECT contains higher quality training examples than previous Split and Rephrase corpora, with sentence splits that require more significant modifications. We categorize examples in our corpus, and use these categories in a novel model that allows us to target specific regions of the input sentence to be split and edited. Moreover, we show that models trained on BISECT can perform a wider variety of split operations and improve upon previous state-of-the-art approaches in automatic and human evaluations. 1

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ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences

Cited by 2 publications

References 26 publications

Hierarchical Clause Annotation: Building a Clause-Level Corpus for Semantic Parsing with Complex Sentences

Hierarchical Clause Annotation: Building a Clause-Level Corpus for Semantic Parsing with Complex Sentences

BiSECT: Learning to Split and Rephrase Sentences with Bitexts

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