Text Simplification Using Neural Machine Translation

Tong Wang, Ping Chen, John Rochford, Jipeng Qiang

AAAI 2016 pp. 4270-4271

doi:10.1609/AAAI.V30I1.9933 /aaai/2016/wang2016aaai-text/

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.

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Text

Wang et al. "Text Simplification Using Neural Machine Translation." AAAI Conference on Artificial Intelligence, 2016. doi:10.1609/AAAI.V30I1.9933

Markdown

[Wang et al. "Text Simplification Using Neural Machine Translation." AAAI Conference on Artificial Intelligence, 2016.](https://mlanthology.org/aaai/2016/wang2016aaai-text/) doi:10.1609/AAAI.V30I1.9933

BibTeX

@inproceedings{wang2016aaai-text,
  title     = {{Text Simplification Using Neural Machine Translation}},
  author    = {Wang, Tong and Chen, Ping and Rochford, John and Qiang, Jipeng},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2016},
  pages     = {4270-4271},
  doi       = {10.1609/AAAI.V30I1.9933},
  url       = {https://mlanthology.org/aaai/2016/wang2016aaai-text/}
}