Neural versus non-neural Text Simplification: a case study

Islam Nassar; Michelle Ananda-Rajah; Reza Haffari

Neural versus non-neural Text Simplification: a case study

Islam Nassar, Michelle Ananda-Rajah, Reza Haffari

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research

Abstract

We propose a modular rule-based system for Text Simplification and show that it out- performs the state-of-the-art neural-based simplification system in terms of simplic- ity. We compare the output of both systems to highlight the differences between the two approaches. Further, we present an ad- aptation of our system to handle domain- specific tasks, where we employ a hybrid approach of our rule-based system and phrase-based machine translation to sim- plify medical discharge summaries in a low-resource situation. We compile a small medical simplification dataset to evaluate our proposed solution.

Original language	English
Title of host publication	Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association
Editors	Meladel Mistica
Place of Publication	Stroudsburg PA USA
Publisher	Association for Computational Linguistics (ACL)
Pages	172–177
Number of pages	6
Publication status	Published - 2019
Event	Australasian Language Technology Association Workshop 2019 - Sydney, Australia Duration: 4 Nov 2019 → 6 Nov 2019 Conference number: 17th https://aclanthology.org/volumes/U19-1/ (Proceedings)

Conference

Conference	Australasian Language Technology Association Workshop 2019
Abbreviated title	ALTA 2019
Country/Territory	Australia
City	Sydney
Period	4/11/19 → 6/11/19
Internet address	https://aclanthology.org/volumes/U19-1/ (Proceedings)

Access to Document

361471591-oaFinal published version, 369 KB

https://aclanthology.org/U19-1023.pdf

Exploiting Context in Multilingual Understanding and Generation
Haffari, R.
Australian Research Council (ARC)
20/11/20 → 28/02/26
Project: Research

Cite this

@inproceedings{0bcaab67a7b84368a7d75d0f34791198,

title = "Neural versus non-neural Text Simplification: a case study",

abstract = "We propose a modular rule-based system for Text Simplification and show that it out- performs the state-of-the-art neural-based simplification system in terms of simplic- ity. We compare the output of both systems to highlight the differences between the two approaches. Further, we present an ad- aptation of our system to handle domain- specific tasks, where we employ a hybrid approach of our rule-based system and phrase-based machine translation to sim- plify medical discharge summaries in a low-resource situation. We compile a small medical simplification dataset to evaluate our proposed solution.",

author = "Islam Nassar and Michelle Ananda-Rajah and Reza Haffari",

year = "2019",

language = "English",

pages = "172–177",

editor = "Meladel Mistica",

booktitle = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",

publisher = "Association for Computational Linguistics (ACL)",

note = "Australasian Language Technology Association Workshop 2019, ALTA 2019 ; Conference date: 04-11-2019 Through 06-11-2019",

url = "https://aclanthology.org/volumes/U19-1/",

}

Nassar, I, Ananda-Rajah, M & Haffari, R 2019, Neural versus non-neural Text Simplification: a case study. in M Mistica (ed.), Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association., U19-1023, Association for Computational Linguistics (ACL), Stroudsburg PA USA, pp. 172–177, Australasian Language Technology Association Workshop 2019, Sydney, New South Wales, Australia, 4/11/19. <https://aclanthology.org/U19-1023.pdf>

Neural versus non-neural Text Simplification: a case study. / Nassar, Islam; Ananda-Rajah, Michelle; Haffari, Reza.
Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association. ed. / Meladel Mistica. Stroudsburg PA USA: Association for Computational Linguistics (ACL), 2019. p. 172–177 U19-1023.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research

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T1 - Neural versus non-neural Text Simplification

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AB - We propose a modular rule-based system for Text Simplification and show that it out- performs the state-of-the-art neural-based simplification system in terms of simplic- ity. We compare the output of both systems to highlight the differences between the two approaches. Further, we present an ad- aptation of our system to handle domain- specific tasks, where we employ a hybrid approach of our rule-based system and phrase-based machine translation to sim- plify medical discharge summaries in a low-resource situation. We compile a small medical simplification dataset to evaluate our proposed solution.

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EP - 177

BT - Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association

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ER -

Neural versus non-neural Text Simplification: a case study

Abstract

Conference

Access to Document

Projects

Exploiting Context in Multilingual Understanding and Generation

Cite this