Sentence-Level Grammatical Error Identification as Sequence-to-Sequence Correction

We demonstrate that an attention-based encoder-decoder model can be used for sentence-level grammatical error identification for the Automated Evaluation of Scientific Writing (AESW) Shared Task 2016. The attention-based encoder-decoder models can be used for the generation of corrections, in addition to error identification, which is of interest for certain end-user applications. We show that a character-based encoder-decoder model is particularly effective, outperforming other results on the AESW Shared Task on its own, and showing gains over a word-based counterpart. Our final model--a combination of three character-based encoder-decoder models, one word-based encoder-decoder model, and a sentence-level CNN--is the highest performing system on the AESW 2016 binary prediction Shared Task.

[1]  Hwee Tou Ng,et al.  The CoNLL-2013 Shared Task on Grammatical Error Correction , 2013, CoNLL Shared Task.

[2]  Alexander M. Rush,et al.  Character-Aware Neural Language Models , 2015, AAAI.

[3]  Rafael E. Banchs,et al.  A Report on the Automatic Evaluation of Scientific Writing Shared Task , 2016, BEA@NAACL-HLT.

[4]  Yoon Kim,et al.  Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.

[5]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[6]  Adam Kilgarriff,et al.  Helping Our Own: The HOO 2011 Pilot Shared Task , 2011, ENLG.

[7]  Yoshua Bengio,et al.  On Using Monolingual Corpora in Neural Machine Translation , 2015, ArXiv.

[8]  Christopher D. Manning,et al.  Effective Approaches to Attention-based Neural Machine Translation , 2015, EMNLP.

[9]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[10]  Matt Post,et al.  Ground Truth for Grammatical Error Correction Metrics , 2015, ACL.

[11]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[12]  Wojciech Zaremba,et al.  Recurrent Neural Network Regularization , 2014, ArXiv.

[13]  Roger Levy,et al.  Automated Whole Sentence Grammar Correction Using a Noisy Channel Model , 2011, ACL.

[14]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[15]  Ye Zhang,et al.  A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification , 2015, IJCNLP.

[16]  Nizar Habash,et al.  The Illinois-Columbia System in the CoNLL-2014 Shared Task , 2014, CoNLL Shared Task.

[17]  Helen Yannakoudakis,et al.  Grammatical error correction using hybrid systems and type filtering , 2014, CoNLL Shared Task.

[18]  Robert Dale,et al.  HOO 2012: A Report on the Preposition and Determiner Error Correction Shared Task , 2012, BEA@NAACL-HLT.

[19]  Quoc V. Le,et al.  Semi-supervised Sequence Learning , 2015, NIPS.

[20]  Rico Sennrich,et al.  Improving Neural Machine Translation Models with Monolingual Data , 2015, ACL.

[21]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[22]  Maria Leonor Pacheco,et al.  of the Association for Computational Linguistics: , 2001 .

[23]  Jürgen Schmidhuber,et al.  Training Very Deep Networks , 2015, NIPS.

[24]  Raymond Hendy Susanto,et al.  The CoNLL-2014 Shared Task on Grammatical Error Correction , 2014 .