Papers with low-resource neural MT
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. |