UC San Diego

Automated grammar correction techniques have seen improvement over the years, but there is still much room for increased performance. Current correction techniques mainly focus on identifying and correcting a specific type of error, such as verb form misuse or preposition misuse, which restricts the corrections to a limited scope. We introduce a novel technique, based on a noisy channel model, which can utilize the whole sentence context to determine proper corrections. We show how to use the EM algorithm to learn the parameters of the noise model, using only a data set of erroneous sentences, given the proper language model. This frees us from the burden of acquiring a large corpora of corrected sentences. We also present a cheap and efficient way to provide automated evaluation results for grammar corrections by using BLEU and METEOR, in contrast to the commonly used manual evaluation