Neural and FST-based approaches to grammatical error correction
Zheng Yuan, Felix Stahlberg, Marek Rei, Bill Byrne, Helen Yannakoudakis · 2019
In this paper, we describe our submission to the BEA 2019 shared task on grammatical error correction.We present a system pipeline that utilises both error detection and correction models.The input text is first corrected by two complementary neural machine translation systems: one using convolutional networks and multi-task learning, and another using a neural Transformer-based system.Training is performed on publicly available data, along with artificial examples generated through back-translation.The n-best lists of these two machine translation systems are then combined and scored using a finite state transducer (FST).Finally, an unsupervised reranking system is applied to the n-best output of the FST.The re-ranker uses a number of error detection features to re-rank the FST nbest list and identify the final 1-best correction hypothesis.Our system achieves 66.75% F 0.5 on error correction (ranking 4th), and 82.52% F 0.5 on token-level error detection (ranking 2nd) in the restricted track of the shared task.