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Neural sequence-to-sequence models are finding increasing use in editing of documents, for example in correcting a text document or repairing source code.
SequenceR: Sequence-to-sequence learning for end-to-end program repair
Chen, Z.; Kommrusch, S.; Tufano, M.; Pouchet, L.-N.; Poshyvanyk, D.; and Monperrus, M. 2018 · 1901
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Insertion transformer: Flexible sequence generation via insertion operations
Stern, M.; Chan, W.; Kiros, J.; and Uszkoreit, J. 2019 · 1902
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Non-monotonic sequential text generation
Welleck, S.; Brantley, K.; Daumé III, H.; and Cho, K. 2019 · 1902
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Gu, J.; Wang, C.; and Zhao, J. 2019 · 1905
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ENCORE: Ensemble Learning using Convolution Neural Machine Translation for Automatic Program Repair
Lutellier, T.; Pang, L.; Pham, H. V.; Wei, M.; and Tan, L. 2019 · 1906
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The computer learner corpus: a versatile new source of data for SLA research
Granger, S. 1998 · 1998
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A new dataset and method for automatically grading ESOL texts
Yannakoudakis, H.; Briscoe, T.; and Medlock, B. 2011 · 2011
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Sequence to sequence learning with neural networks
Sutskever, I.; Vinyals, O.; and Le, Q. V. 2014 · 2014
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Effective approaches to attention-based neural machine translation
Luong, M.-T.; Pham, H.; and Manning, C. D. 2015 · 2015
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Pointer networks
Vinyals, O.; Fortunato, M.; and Jaitly, N. 2015 · 2015
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Incorporating copying mechanism in sequence-to-sequence learning
Gu, J.; Lu, Z.; Li, H.; and Li, V. O. 2016 · 2016
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Pointing the unknown words
Gulcehre, C.; Ahn, S.; Nallapati, R.; Zhou, B.; and Bengio, Y. 2016 · 2016
Cited alongside, same era.
Latent predictor networks for code generation
Ling, W.; Grefenstette, E.; Hermann, K. M.; Kočiskỳ, T.; Senior, A.; Wang, F.; and Blunsom, P. 2016 · 2016
Cited alongside, same era.
Automatic annotation and evaluation of error types for grammatical error correction
Bryant, C.; Felice, M.; and Briscoe, E. J. 2017 · 2017
Cited alongside, same era.
QuickEdit: Editing text & translations by crossing words out
Grangier, D.; and Auli, M. 2017 · 2017
Cited alongside, same era.
Improving neural language models with a continuous cache
Grave, E.; Joulin, A.; and Usunier, N. 2017 · 2017
Cited alongside, same era.
Deterministic non-autoregressive neural sequence modeling by iterative refinement
Lee, J.; Mansimov, E.; and Cho, K. 2018 · 2018
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Constrained Graph Variational Autoencoders for Molecule Design
Liu, Q.; Allamanis, M.; Brockschmidt, M.; and Gaunt, A. L. 2018 · 2018
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Automatic software repair: a bibliography
Monperrus, M. 2018 · 2018
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Zhou, Q.; Yang, N.; Wei, F.; and Zhou, M. 2018 · 2018
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The BEA-2019 shared task on grammatical error correction
Bryant, C.; Felice, M.; Andersen, Ø. E.; and Briscoe, T. 2019 · 2019
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Deepfix: Fixing common C language errors by deep learning
Gupta, R.; Pal, S.; Kanade, A.; and Shevade, S. 2017 · 2017
Cited alongside, same era.
Pointer sentinel mixture models
Merity, S.; Xiong, C.; Bradbury, J.; and Socher, R. 2017 · 2017
Cited alongside, same era.
Get To The Point: Summarization with Pointer-Generator Networks
See, A.; Liu, P. J.; and Manning, C. D. 2017 · 2017
Cited alongside, same era.
Multiscale sequence modeling with a learned dictionary
van Merriënboer, B.; Sanyal, A.; Larochelle, H.; and Bengio, Y. 2017 · 2017
Cited alongside, same era.
WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse
Faruqui, M.; Pavlick, E.; Tenney, I.; and Das, D. 2018 · 2018
Cited alongside, same era.
Grave, É.; Sukhbaatar, S.; Bojanowski, P.; and Joulin, A. 2019 · 2019
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Neural Grammatical Error Correction Systems with Unsupervised Pre-training on Synthetic Data
Grundkiewicz, R.; Junczys-Dowmunt, M.; and Heafield, K. 2019 · 2019
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An empirical study on learning bug-fixing patches in the wild via neural machine translation
Tufano, M.; Watson, C.; Bavota, G.; Penta, M. D.; White, M.; and Poshyvanyk, D. 2019 · 2019
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Learning to represent edits
Yin, P.; Neubig, G.; Allamanis, M.; Brockschmidt, M.; and Gaunt, A. L. 2019 · 2019
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A convolutional attention network for extreme summarization of source code
Allamanis, M.; Peng, H.; and Sutton, C. 2016 · 2091
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