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

Sudhanshu Kasewa, Pontus Stenetorp, Sebastian Riedel

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% F0.5 score. When attempting
to determine if a given sentence is
synthetic, a human annotator at best achieves
39.39
F1 score, indicating that our model generates
mostly human-like instances.