Text simplification using neural machine translation

Tong Wang; Ping Chen; John Rochford; Jipeng Qiang

Text simplification using neural machine translation

Tong Wang
, Ping Chen
, John Rochford
, Jipeng Qiang

Engineering

University of Massachusetts Boston
University of Massachusetts Medical School
Hefei University of Technology

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

Abstract

Text simplification (TS) is the technique of reducing the lexical, syntactical complexity of text. Existing automatic TS systems can simplify text only by lexical simplification or by manually defined rules. Neural Machine Translation (NMT) is a recently proposed approach for Machine Translation (MT) that is receiving a lot of research interest. In this paper, we regard original English and simplified English as two languages, and apply a NMT model-Recurrent Neural Network (RNN) encoder-decoder on TS to make the neural network to learn text simplification rules by itself. Then we discuss challenges and strategies about how to apply a NMT model to the task of text simplification.

Original language	English
Title of host publication	30th AAAI Conference on Artificial Intelligence, AAAI 2016
Publisher	AAAI Press
Pages	4270-4271
Number of pages	2
ISBN (Electronic)	9781577357605
State	Published - 2016
Event	30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States Duration: Feb 12 2016 → Feb 17 2016

Publication series

Name	30th AAAI Conference on Artificial Intelligence, AAAI 2016

Conference

Conference	30th AAAI Conference on Artificial Intelligence, AAAI 2016
Country/Territory	United States
City	Phoenix
Period	2/12/16 → 2/17/16

ASJC Scopus Subject Areas

Artificial Intelligence

Cite this

@inproceedings{230aa3d47910467e9d60b8b998eee07c,

title = "Text simplification using neural machine translation",

abstract = "Text simplification (TS) is the technique of reducing the lexical, syntactical complexity of text. Existing automatic TS systems can simplify text only by lexical simplification or by manually defined rules. Neural Machine Translation (NMT) is a recently proposed approach for Machine Translation (MT) that is receiving a lot of research interest. In this paper, we regard original English and simplified English as two languages, and apply a NMT model-Recurrent Neural Network (RNN) encoder-decoder on TS to make the neural network to learn text simplification rules by itself. Then we discuss challenges and strategies about how to apply a NMT model to the task of text simplification.",

author = "Tong Wang and Ping Chen and John Rochford and Jipeng Qiang",

note = "Publisher Copyright: {\textcopyright} Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 30th AAAI Conference on Artificial Intelligence, AAAI 2016 ; Conference date: 12-02-2016 Through 17-02-2016",

year = "2016",

language = "English",

series = "30th AAAI Conference on Artificial Intelligence, AAAI 2016",

publisher = "AAAI Press",

pages = "4270--4271",

booktitle = "30th AAAI Conference on Artificial Intelligence, AAAI 2016",

}

TY - GEN

T1 - Text simplification using neural machine translation

AU - Wang, Tong

AU - Chen, Ping

AU - Rochford, John

AU - Qiang, Jipeng

PY - 2016

Y1 - 2016

N2 - Text simplification (TS) is the technique of reducing the lexical, syntactical complexity of text. Existing automatic TS systems can simplify text only by lexical simplification or by manually defined rules. Neural Machine Translation (NMT) is a recently proposed approach for Machine Translation (MT) that is receiving a lot of research interest. In this paper, we regard original English and simplified English as two languages, and apply a NMT model-Recurrent Neural Network (RNN) encoder-decoder on TS to make the neural network to learn text simplification rules by itself. Then we discuss challenges and strategies about how to apply a NMT model to the task of text simplification.

AB - Text simplification (TS) is the technique of reducing the lexical, syntactical complexity of text. Existing automatic TS systems can simplify text only by lexical simplification or by manually defined rules. Neural Machine Translation (NMT) is a recently proposed approach for Machine Translation (MT) that is receiving a lot of research interest. In this paper, we regard original English and simplified English as two languages, and apply a NMT model-Recurrent Neural Network (RNN) encoder-decoder on TS to make the neural network to learn text simplification rules by itself. Then we discuss challenges and strategies about how to apply a NMT model to the task of text simplification.

UR - https://www.scopus.com/pages/publications/85007174171

UR - https://www.scopus.com/pages/publications/85007174171#tab=citedBy

M3 - Conference contribution

AN - SCOPUS:85007174171

T3 - 30th AAAI Conference on Artificial Intelligence, AAAI 2016

SP - 4270

EP - 4271

BT - 30th AAAI Conference on Artificial Intelligence, AAAI 2016

PB - AAAI Press

T2 - 30th AAAI Conference on Artificial Intelligence, AAAI 2016

Y2 - 12 February 2016 through 17 February 2016

ER -

Text simplification using neural machine translation

Abstract

Publication series

Conference

ASJC Scopus Subject Areas

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Cite this