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.

本文提出一种名为 Sequence-to-Action（S2A）的模型，可以联合输入源和目标句子，并在预测每个标记之前自动生成一个基于标记的动作序列，以提供更好的语法纠错性能。在英语和中文 GEC 任务的基准数据集上进行实验，证明我们的模型相对于基线模型具有更好的性能和生成结果的普适性和多样性。

序列到动作：由动作引导的序列生成进行语法错误更正

Sequence-to-Action: Grammatical Error Correction with Action Guided Sequence Generation

This paper proposes a neural semantic parsing approach -- Sequence-to-Action,
which models semantic parsing as an end-to-end semantic graph generation
process. Our method simultaneously leverages the advantages from two recent
promising directions of semantic parsing. Firstly, our model uses a semantic
graph to represent the meaning of a sentence, which has a tight-coupling with
knowledge bases. Secondly, by leveraging the powerful representation learning
and prediction ability of neural network models, we propose a RNN model which
can effectively map sentences to action sequences for semantic graph
generation. Experiments show that our method achieves state-of-the-art
performance on OVERNIGHT dataset and gets competitive performance on GEO and
ATIS datasets.

本研究提出了一种神经语义解析方法，即 Sequence-to-Action，将语义解析建模为端到端的语义图生成过程，并结合了语义解析的两个方向的优点，通过强大的表征学习和预测能力，提出了一种可以有效地将句子映射到行动序列来生成语义图的 RNN 模型。实验结果表明，该方法在 OVERNIGHT 数据集上达到了最先进的性能，并在 GEO 和 ATIS 数据集上取得了竞争性的表现。