Papers with neural GEC models
Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task (N18-1)
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| Challenge: | Previously, neural methods in grammatical error correction did not reach state-of-the-art results compared to phrase-based statistical machine translation (SMT) systems that improve on results by SMT use their set-up as a backbone for more complex systems. |
| Approach: | They propose a set of model-independent methods for neural GEC that can be easily applied in most GEC settings. |
| Outcome: | The proposed methods outperform state-of-the-art neural GEC systems by 10% M2 on the CoNLL-2014 benchmark and 5.9% on the JFLEG test set. |
Corpora Generation for Grammatical Error Correction (N19-1)
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| Challenge: | Grammatical Error Correction (GEC) is a computational task that requires large amounts of data to solve. |
| Approach: | They propose two approaches to generate large parallel datasets for GEC using publicly available Wikipedia edit histories using minimal filtration heuristics and round-trip translation through bridge languages. |
| Outcome: | The proposed methods yield similar sized parallel corpora with around 4B tokens and are far ahead of the state-of-the-art on the CoNLL ‘14 benchmark and the JFLEG task. |