Controllable Data Synthesis Method for Grammatical Error Correction
This addresses data scarcity for GEC researchers and practitioners, though it is incremental as it builds on existing synthesis techniques with added control.
The paper tackles the lack of parallel data in Grammatical Error Correction (GEC) by proposing two controllable data synthesis methods that manipulate error rates and types, achieving comparable state-of-the-art performance with 100 million synthetic data using half the data of existing methods.
Due to the lack of parallel data in current Grammatical Error Correction (GEC) task, models based on Sequence to Sequence framework cannot be adequately trained to obtain higher performance. We propose two data synthesis methods which can control the error rate and the ratio of error types on synthetic data. The first approach is to corrupt each word in the monolingual corpus with a fixed probability, including replacement, insertion and deletion. Another approach is to train error generation models and further filtering the decoding results of the models. The experiments on different synthetic data show that the error rate is 40% and the ratio of error types is the same can improve the model performance better. Finally, we synthesize about 100 million data and achieve comparable performance as the state of the art, which uses twice as much data as we use.