Exploiting N-Best Hypotheses to Improve an SMT Approach to Grammatical Error Correction

TL;DR

This paper enhances grammatical error correction by leveraging n-best hypotheses from SMT systems, using a classifier to re-rank and select edits, leading to significant accuracy improvements on benchmark datasets.

Contribution

It introduces a novel method that exploits multiple hypotheses from SMT for GEC, improving accuracy through a classifier-based re-ranking approach.

Findings

Achieved statistically significant accuracy improvements

Outperformed previous state-of-the-art GEC systems

Effective use of n-best hypotheses for error correction

Abstract

Grammatical error correction (GEC) is the task of detecting and correcting grammatical errors in texts written by second language learners. The statistical machine translation (SMT) approach to GEC, in which sentences written by second language learners are translated to grammatically correct sentences, has achieved state-of-the-art accuracy. However, the SMT approach is unable to utilize global context. In this paper, we propose a novel approach to improve the accuracy of GEC, by exploiting the n-best hypotheses generated by an SMT approach. Specifically, we build a classifier to score the edits in the n-best hypotheses. The classifier can be used to select appropriate edits or re-rank the n-best hypotheses. We apply these methods to a state-of-the-art GEC system that uses the SMT approach. Our experiments show that our methods achieve statistically significant improvements in accuracy over the best published results on a benchmark test dataset on GEC.