  Grammatical Error Correction (GEC) aims to correct writing errors and help
language learners improve their writing skills. However, existing GEC models
tend to produce spurious corrections or fail to detect lots of errors. The
quality estimation model is necessary to ensure learners get accurate GEC
results and avoid misleading from poorly corrected sentences. Well-trained GEC
models can generate several high-quality hypotheses through decoding, such as
beam search, which provide valuable GEC evidence and can be used to evaluate
GEC quality. However, existing models neglect the possible GEC evidence from
different hypotheses. This paper presents the Neural Verification Network
(VERNet) for GEC quality estimation with multiple hypotheses. VERNet
establishes interactions among hypotheses with a reasoning graph and conducts
two kinds of attention mechanisms to propagate GEC evidence to verify the
quality of generated hypotheses. Our experiments on four GEC datasets show that
VERNet achieves state-of-the-art grammatical error detection performance,
achieves the best quality estimation results, and significantly improves GEC
performance by reranking hypotheses. All data and source codes are available at
https://github.com/thunlp/VERNet.
