Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection

TL;DR

This paper presents a method for generating realistic grammatical errors using sequence-to-sequence models to create synthetic training data, significantly improving grammatical error detection performance.

Contribution

It introduces a cost-effective approach to generate high-quality synthetic errors, enhancing grammatical error detection models beyond previous state-of-the-art results.

Findings

Synthetic error data improves detection accuracy

Achieves over 5% $F_{0.5}$ score improvement

Generated errors are mostly human-like

Abstract

Grammatical error correction, like other machine learning tasks, greatly benefits from large quantities of high quality training data, which is typically expensive to produce. While writing a program to automatically generate realistic grammatical errors would be difficult, one could learn the distribution of naturallyoccurring errors and attempt to introduce them into other datasets. Initial work on inducing errors in this way using statistical machine translation has shown promise; we investigate cheaply constructing synthetic samples, given a small corpus of human-annotated data, using an off-the-rack attentive sequence-to-sequence model and a straight-forward post-processing procedure. Our approach yields error-filled artificial data that helps a vanilla bi-directional LSTM to outperform the previous state of the art at grammatical error detection, and a previously introduced model to gain further improvements of over 5% $F_{0.5}$ score. When attempting to determine if a given sentence is synthetic, a human annotator at best achieves 39.39 $F_1$ score, indicating that our model generates mostly human-like instances.