CoNLL 2014
Related Papers
Phrase-based Machine Translation is State-of-the-Art for Automatic Grammatical Error Correction
In this work, we study parameter tuning to- wards the M2 metric, the standard metric for automatic grammar error correction (GEC) tasks. After implementing M2 as a scorer in the …...
Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction
AbstractUntil now, error type performance for Grammatical Error Correction (GEC) systems could only be measured in terms of recall because system output is not annotated. To overcome this problem, we …...
A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction
AbstractExisting approaches for grammatical error correction (GEC) largely rely on supervised learning with manually created GEC datasets. However, there has been little focus on verifying and ensuring the quality of …...
Improving Grammatical Error Correction with Data Augmentation by Editing Latent Representation
AbstractThe incorporation of data augmentation method in grammatical error correction task has attracted much attention. However, existing data augmentation methods mainly apply noise to tokens, which leads to the lack …...
Frustratingly Easy System Combination for Grammatical Error Correction
AbstractIn this paper, we formulate system combination for grammatical error correction (GEC) as a simple machine learning task: binary classification. We demonstrate that with the right problem formulation, a simple …...