The sequence-to-sequence (Seq2Seq) approach has recently been widely used in
grammatical error correction (GEC) and shows promising performance. However,
the Seq2Seq GEC approach still suffers from two issues. First, a Seq2Seq GEC
model can only be trained on parallel data, which, in GEC task, is often noisy
and limited in quantity. Second, the decoder of a Seq2Seq GEC model lacks an
explicit awareness of the correctness of the token being generated. In this
paper, we propose a unified decoding intervention framework that employs an
external critic to assess the appropriateness of the token to be generated
incrementally, and then dynamically influence the choice of the next token. We
discover and investigate two types of critics: a pre-trained left-to-right
language model critic and an incremental target-side grammatical error detector
critic. Through extensive experiments on English and Chinese datasets, our
framework consistently outperforms strong baselines and achieves results
competitive with state-of-the-art methods.

我们提出了一种统一的解码干预框架，利用外部评论家来评估逐步生成的令牌的适当性，并在动态影响下一个令牌的选择。通过在英语和中文数据集上进行广泛实验，我们的框架始终优于强基准线，并达到与最先进方法相媲美的结果。