Improving Seq2Seq Grammatical Error Correction via Decoding
  Interventions
Houquan Zhou Yumeng Liu Zhenghua Li Min Zhang Bo Zhang Chen Li Ji Zhang Fei Huang
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.