  The task of Grammatical Error Correction (GEC) has received remarkable
attention with wide applications in Natural Language Processing (NLP) in recent
years. While one of the key principles of GEC is to keep the correct parts
unchanged and avoid over-correction, previous sequence-to-sequence (seq2seq)
models generate results from scratch, which are not guaranteed to follow the
original sentence structure and may suffer from the over-correction problem. In
the meantime, the recently proposed sequence tagging models can overcome the
over-correction problem by only generating edit operations, but are conditioned
on human designed language-specific tagging labels. In this paper, we combine
the pros and alleviate the cons of both models by proposing a novel
Sequence-to-Action~(S2A) module. The S2A module jointly takes the source and
target sentences as input, and is able to automatically generate a token-level
action sequence before predicting each token, where each action is generated
from three choices named SKIP, COPY and GENerate. Then the actions are fused
with the basic seq2seq framework to provide final predictions. We conduct
experiments on the benchmark datasets of both English and Chinese GEC tasks.
Our model consistently outperforms the seq2seq baselines, while being able to
significantly alleviate the over-correction problem as well as holding better
generality and diversity in the generation results compared to the sequence
tagging models.
