English as a Second Language (ESL) learners’ writings contain various grammatical errors. Previous research on automatic error correction for ESL learners’ grammatical errors deals with restricted types of learners’ errors. Some types of errors can be corrected by rules using heuristics, while others are difficult to correct without statistical models using native corpora and/or learner corpora...

The Cambridge Learner Corpus is a 16 million-word corpus of Learner English collected by Cambridge University Press in collaboration with the University of Cambridge Local Examinations Syndicate (now Cambridge ESOL). It comprises English examination scripts, transcribed retaining all errors, written by learners of English with 86 different mother tongues. The scripts range across 8 EFL examinat...

This is a collection of papers edited by the founder and coordinator of the International Corpus of Learner English (ICLE), which brings together written texts produced by non-native speakers (NNSs) of English from a variety of European mother-tongue backgrounds. The book is divided into three parts, each composed of several papers: the first part is devoted to a general outline of the constitu...

We are developing and annotating a learner corpus of Hungarian, composed of student journals from three different proficiency levels written at Indiana University. Our annotation marks learner errors that are of different linguistic categories, including phonology, morphology, and syntax, but defining the annotation for an agglutinative language presents several issues. First, we must adapt an ...

We discuss the collection and analysis of a cross-sectional and longitudinal learner corpus consisting of answers to reading comprehension questions written by adult second language learners of German. We motivate the need for such task-based learner corpora and identify the properties which make reading comprehension exercises a particularly interesting task. In terms of the creation of the co...

We present a very simple model for text quality assessment based on a deep convolutional neural network, where the only supervision required is one corpus of usergenerated text of varying quality, and one contrasting text corpus of consistently high quality. Our model is able to provide local quality assessments in different parts of a text, which allows visual feedback about where potentially ...