The field of Grammatical Error Correction (GEC) has produced various systems
to deal with focused phenomena or general text editing. We propose an automatic
way to combine black-box systems. Our method automatically detects the strength
of a system or the combination of several systems per error type, improving
precision and recall while optimizing $F$ score directly. We show consistent
improvement over the best standalone system in all the configurations tested.
This approach also outperforms average ensembling of different RNN models with
random initializations.
In addition, we analyze the use of BERT for GEC - reporting promising results
on this end. We also present a spellchecker created for this task which
outperforms standard spellcheckers tested on the task of spellchecking.
This paper describes a system submission to Building Educational Applications
2019 Shared Task: Grammatical Error Correction.
Combining the output of top BEA 2019 shared task systems using our approach,
currently holds the highest reported score in the open phase of the BEA 2019
shared task, improving F0.5 by 3.7 points over the best result reported.

本研究提出了自动检测系统强度或多个系统组合的方法，在所有测试配置中一致优于最佳独立系统，并对 BERT 的应用进行了分析，同时呈现了对本任务创建的拼写检查器的实验结果。

学习如何结合语法纠错

Learning to combine Grammatical Error Corrections

When applying machine learning to problems in NLP, there are many choices to
make about how to represent input texts. These choices can have a big effect on
performance, but they are often uninteresting to researchers or practitioners
who simply need a module that performs well. We propose an approach to
optimizing over this space of choices, formulating the problem as global
optimization. We apply a sequential model-based optimization technique and show
that our method makes standard linear models competitive with more
sophisticated, expensive state-of-the-art methods based on latent variable
models or neural networks on various topic classification and sentiment
analysis problems. Our approach is a first step towards black-box NLP systems
that work with raw text and do not require manual tuning.

本文介绍了一种基于贝叶斯优化的文本输入表示方法的优化方法，用此方法可以使标准线性模型在各种话题分类和情感分析问题上与基于潜变量模型或神经网络的先进方法相竞争，这种方法是构建原始文本黑盒 NLP 系统的第一步.