Grammatical error correction (GEC) greatly benefits from large quantities of high-quality training data. However, the preparation a amount labelled data is time-consuming and prone to human errors. These issues have become major obstacles in GEC systems. Recently, performance English systems has drastically been enhanced by application deep neural networks that generate synthetic limited sample...

We introduce a novel method for grammatical error correction with a number of small corpora. To make the best use of several corpora with different characteristics, we employ a meta-learning with several base classifiers trained on different corpora. This research focuses on a grammatical error correction task for article errors. A series of experiments is presented to show the effectiveness of...

In this study, we improve grammatical error detection by learning word embeddings that consider grammaticality and error patterns. Most existing algorithms for learning word embeddings usually model only the syntactic context of words so that classifiers treat erroneous and correct words as similar inputs. We address the problem of contextual information by considering learner errors. Specifica...

Abstract The study aimed at investigating the efficacy of written corrective feedback (CF) in improving Iranian EFL learners’ grammatical accuracy. It compared the effects of focused and unfocused written CF on the learners’ grammatical accuracy. 75 EFL students formed a one control and two experimental groups. The focused feedback group was provided with error correction in tenses. The unfocus...

the most important and practical phase of analyzing the linguistic errors of second language learners is to find the solutions to decrease these errors. to that end, the present study examined translation strategy to decrease the number of grammatical and lexical errors of chinese language learners learning persian. twelve chinese language learners were asked to write a text in two stages. firs...