This paper describe the CYUT-III system on grammar error detection in the 2016 NLP-TEA Chinese Grammar Error Detection shared task CGED. In this task a system has to detect four types of errors, including redundant word error, missing word error, word selection error and word ordering error. Based on the conditional random fields (CRF) model, our system is a linear tagger that can detect the er...

This paper describes our submission to the CoNLL 2014 shared task on grammatical error correction using a hybrid approach, which includes both a rule-based and an SMT system augmented by a large webbased language model. Furthermore, we demonstrate that correction type estimation can be used to remove unnecessary corrections, improving precision without harming recall. Our best hybrid system ach...

We propose an approach to N -best list reranking using neural sequence-labelling models. We train a compositional model for error detection that calculates the probability of each token in a sentence being correct or incorrect, utilising the full sentence as context. Using the error detection model, we then re-rank the N best hypotheses generated by statistical machine translation systems. Our ...

Research in automated grammatical error detection and correction has gained considerable momentum in the past few years. Although much progress has been made in this area, numerous challenges and opportunities exist for further research. For NLP researchers and students who are considering following or pursuing work in this area and for English language teaching practitioners and researchers wh...

In this work we adapt machine translation (MT) to grammatical error correction, identifying how components of the statistical MT pipeline can be modified for this task and analyzing how each modification impacts system performance. We evaluate the contribution of each of these components with standard evaluation metrics and automatically characterize the morphological and lexical transformation...

This paper proposes a novel approach to resolve the English article error correction problem, which accounts for a large proportion in grammatical errors. Most previous machine learning based researches empirically collected features which may bring about noises and increase the computational complexity. Meanwhile, the predicted result is largely affected by the threshold setting of a classifie...

Grammatical error correction (GEC) systems strive to correct both global errors in word order and usage, and local errors in spelling and inflection. Further developing upon recent work on neural machine translation, we propose a new hybrid neural model with nested attention layers for GEC. Experiments show that the new model can effectively correct errors of both types by incorporating word an...

This paper describes our ongoing work on grammatical error correction (GEC). Focusing on all possible error types in a real-life environment, we propose a factored statistical machine translation (SMT) model for this task. We consider error correction as a series of language translation problems guided by various linguistic information, as factors that influence translation results. Factors inc...