Data Weighted Training Strategies for Grammatical Error Correction
This work addresses data efficiency for GEC researchers and practitioners, but it is incremental as it builds on existing Neural Machine Translation methods.
The paper tackled the problem of data sparsity in Grammatical Error Correction (GEC) by using example-level scores from a small, high-quality dataset to weight large pretraining data, achieving state-of-the-art results on common GEC test sets.
Recent progress in the task of Grammatical Error Correction (GEC) has been driven by addressing data sparsity, both through new methods for generating large and noisy pretraining data and through the publication of small and higher-quality finetuning data in the BEA-2019 shared task. Building upon recent work in Neural Machine Translation (NMT), we make use of both kinds of data by deriving example-level scores on our large pretraining data based on a smaller, higher-quality dataset. In this work, we perform an empirical study to discover how to best incorporate delta-log-perplexity, a type of example scoring, into a training schedule for GEC. In doing so, we perform experiments that shed light on the function and applicability of delta-log-perplexity. Models trained on scored data achieve state-of-the-art results on common GEC test sets.