---
title: "Parallel Iterative Edit Models for Local Sequence Transduction"
description: "The authors propose PIE (Parallel Iterative Edit), a non-autoregressive model that treats correction… 41 references, cited by 108 papers here."
authors: ["Abhijeet Awasthi", "Sunita Sarawagi", "Rasna Goyal", "Sabyasachi Ghosh", "Vihari Piratla"]
year: 2019
venue: "arXiv:1910.02893"
source: "https://arxiv.org/abs/1910.02893"
doi: "10.48550/arxiv.1910.02893"
arxiv: "1910.02893"
licence: "arXiv — non-exclusive licence to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/awasthi-2019-parallel-iterative-edit-models-local/"
---
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# Parallel Iterative Edit Models for Local Sequence Transduction

**Authors:** [Abhijeet Awasthi](https://md.research.elamur.ai/free/en/authors/abhijeet-awasthi/), Sunita Sarawagi, Rasna Goyal, Sabyasachi Ghosh and Vihari Piratla · **Year:** 2019 · **Venue:** arXiv:1910.02893 · **Licence:** [arXiv — non-exclusive licence to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1910.02893) · [PDF](https://arxiv.org/pdf/1910.02893) · [DOI 10.48550/arxiv.1910.02893](https://doi.org/10.48550/arxiv.1910.02893) · [arXiv 1910.02893](https://arxiv.org/abs/1910.02893) · [OpenAlex](https://openalex.org/W2977610714)

**Citations:** 4 citations (OpenAlex, 2026-10-14) · [108](https://md.research.elamur.ai/free/en/papers/awasthi-2019-parallel-iterative-edit-models-local/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper looks at local sequence transduction, where the output differs from the input in only a few positions. Its main focus is grammatical error correction (GEC). Most strong GEC systems use autoregressive encoder-decoder models, which are accurate but slow because they produce output one token at a time. The authors propose PIE (Parallel Iterative Edit), a non-autoregressive model that treats correction as tagging. Each input token gets an edit label, such as copy, delete, append a q-gram, replace, or a morphological transformation. An algorithm based on a modified Levenshtein alignment turns each training pair into a sequence of edits. To fit edits to a BERT-style bidirectional encoder, PIE splits the output logits into an edit part and a token-argument part. It then feeds its own output back in for further passes, which captures dependencies between edits. On standard GEC benchmarks, PIE comes close to state-of-the-art accuracy and decodes 5–15 times faster than a beam-search encoder-decoder baseline. It also does well on OCR and spelling correction. This shows that parallel decoding can match sequential models when the output stays close to the input.

## Key points
- The edit space includes C (copy), D (delete), A(w) (append), R(w) (replace), and word-inflection transformations (for example, arrive → arrival). Inserts are attached to the edit before them, and the insert vocabulary is limited to the M most frequent q-grams, with q = 2.
- In the NUCLE corpus (about 57.1K sentences), fewer than 3% of sentences need three or more inserts in a row, which supports the bigram limit.
- Pre-trained BERT is reused in the encoder through the factorized logits and edit-specific attention masks. At inference, refinement repeats until the output stops changing or a set iteration limit is reached.
- For OCR correction (a Finnish newspaper corpus with the Silfverberg et al. splits) and spelling correction, a small 4-layer transformer reaches accuracy comparable to encoder-decoder models. It decodes about 80 vs. 37 words/s (spelling) and 65 vs. 43 words/s (OCR) against a Soft-T2T baseline.
- The code and pre-trained GEC models are available at github.com/awasthiabhijeet/PIE.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Indian Institute of Technology Bombay](https://md.research.elamur.ai/free/en/universities/indian-institute-of-technology-bombay/) — University, Mumbai, India · 32 works · 2,535 citations

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## Cite

Cite as: Abhijeet Awasthi, Sunita Sarawagi, Rasna Goyal, Sabyasachi Ghosh and Vihari Piratla. 2019. Parallel Iterative Edit Models for Local Sequence Transduction. arXiv:1910.02893.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{awasthi2019parallel,
  title = {{Parallel Iterative Edit Models for Local Sequence Transduction}},
  author = {Abhijeet Awasthi, Sunita Sarawagi, Rasna Goyal, Sabyasachi Ghosh and Vihari Piratla},
  year = {2019},
  doi = {10.48550/arxiv.1910.02893},
  eprint = {1910.02893},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1910.02893}
}
```

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## References

41 references, 11 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Roee Aharoni and Yoav Goldberg. 2017. [Morphological inflection generation with hard monotonic attention](https://md.research.elamur.ai/free/en/papers/aharoni-2017-morphological-inflection-generation-hard-monotonic/). In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long Papers, pages 2004–2015.
2. Eiji Aramaki. 2010. Typo corpus. luululu.com/tweet/. http:// [find](https://scholar.google.com/scholar?q=Eiji+Aramaki%2E+2010%2E+Typo+corpus%2E+luululu%2Ecom%2Ftweet%2F%2E+http%3A%2F%2F)
3. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. [Neural machine translation by jointly learning to align and translate](https://md.research.elamur.ai/free/en/papers/bahdanau-2014-neural-machine-translation-jointly-learning/). ICLR.
4. Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Phillipp Koehn, and Tony Robinson. 2013. [One billion word benchmark for measuring progress in statistical language modeling](https://md.research.elamur.ai/free/en/papers/chelba-2013-one-billion-word-benchmark-measuring/). arXiv preprint arXiv:1312.3005.
5. Shamil Chollampatt and Hwee Tou Ng. 2018a. [A multilayer convolutional encoder-decoder neural network for grammatical error correction](https://md.research.elamur.ai/free/en/papers/chollampatt-2018-multilayer-convolutional-encoder-decoder-neural/). In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence.
6. Shamil Chollampatt and Hwee Tou Ng. 2018b. Neural quality estimation of grammatical error correction. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2528–2539. [find](https://scholar.google.com/scholar?q=Shamil+Chollampatt+and+Hwee+Tou+Ng%2E+2018b%2E+Neural+quality+estimation+of+grammatical+error+correction%2E+In+Proceedings+of+the+2018+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2C+pages+2)
7. Daniel Dahlmeier and Hwee Tou Ng. 2012. Better evaluation for grammatical error correction. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 568–572. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Daniel+Dahlmeier+and+Hwee+Tou+Ng%2E+2012%2E+Better+evaluation+for+grammatical+error+correction%2E+In+Proceedings+of+the+2012+Conference+of+the+North+American+Chapter+of+the+Association+for+Computational+Lin)
8. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. [Bert: Pre-training of deep bidirectional transformers for language understanding](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/). arXiv preprint arXiv:1810.04805.
9. Mariano Felice, Zheng Yuan, Øistein E Andersen, Helen Yannakoudakis, and Ekaterina Kochmar. 2014. Grammatical error correction using hybrid systems and type filtering. In Proceedings of the Eighteenth Conference on Computational Natural Language Learning: Shared Task, pages 15–24. [find](https://scholar.google.com/scholar?q=Mariano+Felice%2C+Zheng+Yuan%2C+%C3%98istein+E+Andersen%2C+Helen+Yannakoudakis%2C+and+Ekaterina+Kochmar%2E+2014%2E+Grammatical+error+correction+using+hybrid+systems+and+type+filtering%2E+In+Proceedings+of+the+Eighteenth)
10. Tao Ge, Furu Wei, and Ming Zhou. 2018a. Fluency boost learning and inference for neural grammatical error correction. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), volume 1, pages 1055–1065. [find](https://scholar.google.com/scholar?q=Tao+Ge%2C+Furu+Wei%2C+and+Ming+Zhou%2E+2018a%2E+Fluency+boost+learning+and+inference+for+neural+grammatical+error+correction%2E+In+Proceedings+of+the+56th+Annual+Meeting+of+the+Association+for+Computational+Lin)
11. Tao Ge, Furu Wei, and Ming Zhou. 2018b. Reaching human-level performance in automatic grammatical error correction: An empirical study. arXiv preprint arXiv:1807.01270. [find](https://scholar.google.com/scholar?q=Tao+Ge%2C+Furu+Wei%2C+and+Ming+Zhou%2E+2018b%2E+Reaching+human%2Dlevel+performance+in+automatic+grammatical+error+correction%3A+An+empirical+study%2E+arXiv+preprint+arXiv%3A1807%2E01270%2E)
12. Roman Grundkiewicz and Marcin Junczys-Dowmunt. 2018. Near human-level performance in grammatical error correction with hybrid machine translation. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume… [find](https://scholar.google.com/scholar?q=Roman+Grundkiewicz+and+Marcin+Junczys%2DDowmunt%2E+2018%2E+Near+human%2Dlevel+performance+in+grammatical+error+correction+with+hybrid+machine+translation%2E+In+Proceedings+of+the+2018+Conference+of+the+North+Am)
13. Jiatao Gu, James Bradbury, Caiming Xiong, Victor O.K. Li, and Richard Socher. 2018. [Non-autoregressive neural machine translation](https://md.research.elamur.ai/free/en/papers/gu-2017-non-autoregressive-neural-machine-translation/). In International Conference on Learning Representations (ICLR).
14. Jianshu Ji, Qinlong Wang, Kristina Toutanova, Yongen Gong, Steven Truong, and Jianfeng Gao. 2017. A nested attention neural hybrid model for grammatical error correction. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 753– 76… [find](https://scholar.google.com/scholar?q=Jianshu+Ji%2C+Qinlong+Wang%2C+Kristina+Toutanova%2C+Yongen+Gong%2C+Steven+Truong%2C+and+Jianfeng+Gao%2E+2017%2E+A+nested+attention+neural+hybrid+model+for+grammatical+error+correction%2E+In+Proceedings+of+the+55th+An)
15. Marcin Junczys-Dowmunt and Roman Grundkiewicz. 2016. Phrase-based machine translation is state-ofthe-art for automatic grammatical error correction. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1546–1556. [find](https://scholar.google.com/scholar?q=Marcin+Junczys%2DDowmunt+and+Roman+Grundkiewicz%2E+2016%2E+Phrase%2Dbased+machine+translation+is+state%2Dofthe%2Dart+for+automatic+grammatical+error+correction%2E+In+Proceedings+of+the+2016+Conference+on+Empirical+)
16. Marcin Junczys-Dowmunt, Roman Grundkiewicz, Shubha Guha, and Kenneth Heafield. 2018. Approaching neural grammatical error correction as a low-resource machine translation task. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human… [find](https://scholar.google.com/scholar?q=Marcin+Junczys%2DDowmunt%2C+Roman+Grundkiewicz%2C+Shubha+Guha%2C+and+Kenneth+Heafield%2E+2018%2E+Approaching+neural+grammatical+error+correction+as+a+low%2Dresource+machine+translation+task%2E+In+Proceedings+of+the+2)
17. Zhu Kaili, Chuan Wang, Ruobing Li, Yang Liu, Tianlei Hu, and Hui Lin. 2018. A simple but effective classification model for grammatical error correction. arXiv preprint arXiv:1807.00488. — [arXiv:1807.00488](https://arxiv.org/abs/1807.00488) [find](https://scholar.google.com/scholar?q=Zhu+Kaili%2C+Chuan+Wang%2C+Ruobing+Li%2C+Yang+Liu%2C+Tianlei+Hu%2C+and+Hui+Lin%2E+2018%2E+A+simple+but+effective+classification+model+for+grammatical+error+correction%2E+arXiv+preprint+arXiv%3A1807%2E00488%2E+%E2%80%94+%5BarXiv%3A1807)
18. Lukasz Kaiser, Samy Bengio, Aurko Roy, Ashish Vaswani, Niki Parmar, Jakob Uszkoreit, and Noam Shazeer. 2018. Fast decoding in sequence models using discrete latent variables. In Proceedings of the 35th International Conference on Machine Learning, pages 2390–2399. [find](https://scholar.google.com/scholar?q=Lukasz+Kaiser%2C+Samy+Bengio%2C+Aurko+Roy%2C+Ashish+Vaswani%2C+Niki+Parmar%2C+Jakob+Uszkoreit%2C+and+Noam+Shazeer%2E+2018%2E+Fast+decoding+in+sequence+models+using+discrete+latent+variables%2E+In+Proceedings+of+the+35t)
19. Satoshi Koide, Keisuke Kawano, and Takuro Kutsuna. 2018. Neural edit operations for biological sequences. In Advances in Neural Information Processing Systems 31. [find](https://scholar.google.com/scholar?q=Satoshi+Koide%2C+Keisuke+Kawano%2C+and+Takuro+Kutsuna%2E+2018%2E+Neural+edit+operations+for+biological+sequences%2E+In+Advances+in+Neural+Information+Processing+Systems+31%2E)
20. D. Koller and N. Friedman. 2009. Probabilistic Graphical Models: Principles and Techniques. MIT Press. [find](https://scholar.google.com/scholar?q=D%2E+Koller+and+N%2E+Friedman%2E+2009%2E+Probabilistic+Graphical+Models%3A+Principles+and+Techniques%2E+MIT+Press%2E)
21. Jason Lee, Elman Mansimov, and Kyunghyun Cho. 2018. [Deterministic non-autoregressive neural sequence modeling by iterative refinement](https://md.research.elamur.ai/free/en/papers/lee-2018-deterministic-non-autoregressive-neural-sequence/). In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.
22. Vladimir I Levenshtein. 1966. Binary codes capable of correcting deletions, insertions, and reversals. In Soviet physics doklady, volume 10, pages 707–710. [find](https://scholar.google.com/scholar?q=Vladimir+I+Levenshtein%2E+1966%2E+Binary+codes+capable+of+correcting+deletions%2C+insertions%2C+and+reversals%2E+In+Soviet+physics+doklady%2C+volume+10%2C+pages+707%E2%80%93710%2E)
23. Jared Lichtarge, Chris Alberti, Shankar Kumar, Noam Shazeer, Niki Parmar, and Simon Tong. 2019. Corpora generation for grammatical error correction. arXiv preprint arXiv:1904.05780. [find](https://scholar.google.com/scholar?q=Jared+Lichtarge%2C+Chris+Alberti%2C+Shankar+Kumar%2C+Noam+Shazeer%2C+Niki+Parmar%2C+and+Simon+Tong%2E+2019%2E+Corpora+generation+for+grammatical+error+correction%2E+arXiv+preprint+arXiv%3A1904%2E05780%2E)
24. Tomoya Mizumoto, Mamoru Komachi, Masaaki Nagata, and Yuji Matsumoto. 2011. Mining revision log of language learning sns for automated japanese error correction of second language learners. In Proceedings of 5th International Joint Conference on Natural Language Processing, pages 147–155. [find](https://scholar.google.com/scholar?q=Tomoya+Mizumoto%2C+Mamoru+Komachi%2C+Masaaki+Nagata%2C+and+Yuji+Matsumoto%2E+2011%2E+Mining+revision+log+of+language+learning+sns+for+automated+japanese+error+correction+of+second+language+learners%2E+In+Proceedi)
25. Courtney Napoles, Keisuke Sakaguchi, Matt Post, and Joel Tetreault. 2016. Gleu without tuning. arXiv preprint arXiv:1605.02592. [find](https://scholar.google.com/scholar?q=Courtney+Napoles%2C+Keisuke+Sakaguchi%2C+Matt+Post%2C+and+Joel+Tetreault%2E+2016%2E+Gleu+without+tuning%2E+arXiv+preprint+arXiv%3A1605%2E02592%2E)
26. Courtney Napoles, Keisuke Sakaguchi, and Joel Tetreault. 2017. Jfleg: A fluency corpus and benchmark for grammatical error correction. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 229–234, Valencia, Spai… [find](https://scholar.google.com/scholar?q=Courtney+Napoles%2C+Keisuke+Sakaguchi%2C+and+Joel+Tetreault%2E+2017%2E+Jfleg%3A+A+fluency+corpus+and+benchmark+for+grammatical+error+correction%2E+In+Proceedings+of+the+15th+Conference+of+the+European+Chapter+of+)
27. Hwee Tou Ng, Siew Mei Wu, Ted Briscoe, Christian Hadiwinoto, Raymond Hendy Susanto, and Christopher Bryant. 2014. The CoNLL-2014 shared task on grammatical error correction. In Proceedings of the Eighteenth Conference on Computational Natural Language Learning: Shared Task, pages 1–14. [find](https://scholar.google.com/scholar?q=Hwee+Tou+Ng%2C+Siew+Mei+Wu%2C+Ted+Briscoe%2C+Christian+Hadiwinoto%2C+Raymond+Hendy+Susanto%2C+and+Christopher+Bryant%2E+2014%2E+The+CoNLL%2D2014+shared+task+on+grammatical+error+correction%2E+In+Proceedings+of+the+Eigh)
28. Hwee Tou Ng, Siew Mei Wu, Yuanbin Wu, Christian Hadiwinoto, and Joel Tetreault. 2013. The conll-2013 shared task on grammatical error correction. In Proceedings of the Seventeenth Conference on Computational Natural Language Learning: Shared Task, pages 1–12, Sofia, Bulgaria. Association for Computa… [find](https://scholar.google.com/scholar?q=Hwee+Tou+Ng%2C+Siew+Mei+Wu%2C+Yuanbin+Wu%2C+Christian+Hadiwinoto%2C+and+Joel+Tetreault%2E+2013%2E+The+conll%2D2013+shared+task+on+grammatical+error+correction%2E+In+Proceedings+of+the+Seventeenth+Conference+on+Comput)
29. Aaron van den Oord, Yazhe Li, Igor Babuschkin, Karen Simonyan, Oriol Vinyals, Koray Kavukcuoglu, George van den Driessche, Edward Lockhart, Luis Cobo, Florian Stimberg, Norman Casagrande, Dominik Grewe, Seb Noury, Sander Dieleman, Erich Elsen, Nal Kalchbrenner, Heiga Zen, Alex Graves, Helen King, To… [find](https://scholar.google.com/scholar?q=Aaron+van+den+Oord%2C+Yazhe+Li%2C+Igor+Babuschkin%2C+Karen+Simonyan%2C+Oriol+Vinyals%2C+Koray+Kavukcuoglu%2C+George+van+den+Driessche%2C+Edward+Lockhart%2C+Luis+Cobo%2C+Florian+Stimberg%2C+Norman+Casagrande%2C+Dominik+Grew)
30. Joana Ribeiro, Shashi Narayan, Shay B. Cohen, and Xavier Carreras. 2018. Local string transduction as sequence labeling. In Proceedings of the 27th International Conference on Computational Linguistics (ACL-COLING). [find](https://scholar.google.com/scholar?q=Joana+Ribeiro%2C+Shashi+Narayan%2C+Shay+B%2E+Cohen%2C+and+Xavier+Carreras%2E+2018%2E+Local+string+transduction+as+sequence+labeling%2E+In+Proceedings+of+the+27th+International+Conference+on+Computational+Linguistic)
31. Alla Rozovskaya and Dan Roth. 2016. Grammatical error correction: Machine translation and classifiers. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), volume 1, pages 2205–2215. [find](https://scholar.google.com/scholar?q=Alla+Rozovskaya+and+Dan+Roth%2E+2016%2E+Grammatical+error+correction%3A+Machine+translation+and+classifiers%2E+In+Proceedings+of+the+54th+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28Volum)
32. Allen Schmaltz, Yoon Kim, Alexander M. Rush, and Stuart M. Shieber. 2017. [Adapting sequence models for sentence correction](https://md.research.elamur.ai/free/en/papers/schmaltz-2017-adapting-sequence-models-sentence-correction/). In EMNLP, pages 2807– 2813.
33. Miikka Silfverberg, Pekka Kauppinen, Krister Lindén, et al. 2016. Data-driven spelling correction using weighted finite-state methods. In The 54th Annual Meeting of the Association for Computational Linguistics Proceedings of the SIGFSM Workshop on Statistical NLP and Weighted Automata. ACL. [find](https://scholar.google.com/scholar?q=Miikka+Silfverberg%2C+Pekka+Kauppinen%2C+Krister+Linde%CC%81n%2C+et+al%2E+2016%2E+Data%2Ddriven+spelling+correction+using+weighted+finite%2Dstate+methods%2E+In+The+54th+Annual+Meeting+of+the+Association+for+Computational+)
34. Felix Stahlberg, Christopher Bryant, and Bill Byrne. 2019. Neural grammatical error correction with finite state transducers. arXiv preprint arXiv:1903.10625. [find](https://scholar.google.com/scholar?q=Felix+Stahlberg%2C+Christopher+Bryant%2C+and+Bill+Byrne%2E+2019%2E+Neural+grammatical+error+correction+with+finite+state+transducers%2E+arXiv+preprint+arXiv%3A1903%2E10625%2E)
35. Mitchell Stern, Noam Shazeer, and Jakob Uszkoreit. 2018. Blockwise parallel decoding for deep autoregressive models. In Advances in Neural Information Processing Systems 31. [find](https://scholar.google.com/scholar?q=Mitchell+Stern%2C+Noam+Shazeer%2C+and+Jakob+Uszkoreit%2E+2018%2E+Blockwise+parallel+decoding+for+deep+autoregressive+models%2E+In+Advances+in+Neural+Information+Processing+Systems+31%2E)
36. Ziang Xie, Anand Avati, Naveen Arivazhagan, Dan Jurafsky, and Andrew Y. Ng. 2016. [Neural language correction with character-based attention](https://md.research.elamur.ai/free/en/papers/xie-2016-neural-language-correction-character-based/). CoRR, abs/1603.09727.
37. Helen Yannakoudakis, Ted Briscoe, and Ben Medlock. 2011. [A new dataset and method for automatically grading esol texts](https://md.research.elamur.ai/free/en/papers/yannakoudakis-2011-new-dataset-method-automatically-grading/). In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pages 180–189. Association for Computational Lingu…
38. Helen Yannakoudakis, Marek Rei, Øistein E Andersen, and Zheng Yuan. 2017. Neural sequence-labelling models for grammatical error correction. In EMNLP, pages 2795–2806. [find](https://scholar.google.com/scholar?q=Helen+Yannakoudakis%2C+Marek+Rei%2C+%C3%98istein+E+Andersen%2C+and+Zheng+Yuan%2E+2017%2E+Neural+sequence%2Dlabelling+models+for+grammatical+error+correction%2E+In+EMNLP%2C+pages+2795%E2%80%932806%2E)
39. Pengcheng Yin, Graham Neubig, Miltiadis Allamanis, Marc Brockschmidt, and Alexander L. Gaunt. 2019. Learning to represent edits. In International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Pengcheng+Yin%2C+Graham+Neubig%2C+Miltiadis+Allamanis%2C+Marc+Brockschmidt%2C+and+Alexander+L%2E+Gaunt%2E+2019%2E+Learning+to+represent+edits%2E+In+International+Conference+on+Learning+Representations%2E)
40. Wei Zhao, Liang Wang, Kewei Shen, Ruoyu Jia, and Jingming Liu. 2019. Improving grammatical error correction via pre-training a copy-augmented architecture with unlabeled data. arXiv preprint arXiv:1903.00138. [find](https://scholar.google.com/scholar?q=Wei+Zhao%2C+Liang+Wang%2C+Kewei+Shen%2C+Ruoyu+Jia%2C+and+Jingming+Liu%2E+2019%2E+Improving+grammatical+error+correction+via+pre%2Dtraining+a+copy%2Daugmented+architecture+with+unlabeled+data%2E+arXiv+preprint+arXiv%3A190)
41. Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015. [Aligning books and movies: Towards story-like visual explanations by watching movies and reading books](https://md.research.elamur.ai/free/en/papers/zhu-2015-aligning-books-movies-towards-story/). In Proceedings of the IEEE international conference on computer vision, pages 19–…

*Updated 2 November 2026*