Grammatical error correction using neural machine translation
Zheng Yuan,Ted Briscoe +1 more
- 01 Jun 2016
- pp 380-386
TL;DR: This paper presents the first study using neural machine translation (NMT) for grammatical error correction (GEC) with a twostep approach to handle the rare word problem in NMT, which has been proved to be useful and effective for the GEC task.
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Abstract: This paper presents the first study using neural machine translation (NMT) for grammatical error correction (GEC). We propose a twostep approach to handle the rare word problem in NMT, which has been proved to be useful and effective for the GEC task. Our best NMTbased system trained on the CLC outperforms our SMT-based system when testing on the publicly available FCE test set. The same system achieves an F0.5 score of 39.90% on the CoNLL-2014 shared task test set, outperforming the state-of-the-art and demonstrating that the NMT-based GEC system generalises effectively.
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References
Bleu: a Method for Automatic Evaluation of Machine Translation
Kishore Papineni,Salim Roukos,Todd Ward,Wei-Jing Zhu +3 more
- 06 Jul 2002
TL;DR: This paper proposed a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run.
Learning Phrase Representations using RNN Encoder--Decoder for Statistical Machine Translation
Kyunghyun Cho,Bart van Merriënboer,Caglar Gulcehre,Dzmitry Bahdanau,Fethi Bougares,Holger Schwenk,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio +8 more
- 01 Jan 2014
TL;DR: In this paper, the encoder and decoder of the RNN Encoder-Decoder model are jointly trained to maximize the conditional probability of a target sequence given a source sequence.
•Proceedings Article
Neural Machine Translation by Jointly Learning to Align and Translate
Dzmitry Bahdanau,Kyunghyun Cho,Yoshua Bengio +2 more
- 01 Jan 2015
TL;DR: It is conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and it is proposed to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.
25.7K
•Posted Content
Neural Machine Translation by Jointly Learning to Align and Translate
TL;DR: In this paper, the authors propose to use a soft-searching model to find the parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.
20.9K
•Proceedings Article
Sequence to Sequence Learning with Neural Networks
Ilya Sutskever,Oriol Vinyals,Quoc V. Le +2 more
- 08 Dec 2014
TL;DR: The authors used a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector.
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