We develop a supervised ranking model to rerank candidates generated from an SMT-based grammatical error correction (GEC) system. A range of novel features with respect to GEC are investigated and implemented in our reranker. We train a rank preference SVM model and demonstrate that this outperforms both Minimum Bayes-Risk and Multi-Engine Machine Translation based re-ranking for the GEC task. ...

Cognitive control involves not only the ability to manage competing task demands, but also the ability to adapt task performance during learning. This study investigated how violation-, response-, and feedback-related electrophysiological (EEG) activity changes over time during language learning. Twenty-two Dutch learners of German classified short prepositional phrases presented serially as te...

In this report, we describe some of the issues encountered when preprocessing two of the largest datasets for Grammatical Error Correction (GEC); namely the public FCE corpus and NUCLE (along with associated CoNLL test sets). In particular, we show that it is not straightforward to convert character level annotations to token level annotations and that sentence segmentation is more complex when...

This paper describes the Nara Institute of Science and Technology (NAIST) error correction system in the CoNLL 2013 Shared Task. We constructed three systems: a system based on the Treelet Language Model for verb form and subjectverb agreement errors; a classifier trained on both learner and native corpora for noun number errors; a statistical machine translation (SMT)-based model for prepositi...

Automatic speech recognition (ASR) of non-native utterances with grammatical errors is problematic. A new method which makes it possible to better recognize such utterances is presented in the current paper. It can be briefly summarized as follows: extract error patterns automatically from a learner corpus, formulate rewrite rules for these syntactic and morphological errors, build finite state...

This paper describes our use of phrasebased statistical machine translation (PBSMT) for the automatic correction of errors in learner text in our submission to the CoNLL 2013 Shared Task on Grammatical Error Correction. Since the limited training data provided for the task was insufficient for training an effective SMT system, we also explored alternative ways of generating pairs of incorrect a...

Some grammatical error detection methods, including the ones currently used by the Educational Testing Service’s e-rater system (Attali and Burstein, 2006), are tuned for precision because of the perceived high cost of false positives (i.e., marking fluent English as ungrammatical). Precision, however, is not optimal for all tasks, particularly the HOO 2012 Shared Task on grammatical errors, wh...

This paper describes the NLP 2 CT Grammatical Error Detection and Correction system for the CoNLL 2013 shared task, with a focus on the errors of article or determiner (ArtOrDet), noun number (Nn), preposition (Prep), verb form (Vform) and subject-verb agreement (SVA). A hybrid model is adopted for this special task. The process starts with spellchecking as a preprocessing step to correct any p...

We present a new parallel corpus, JHU FLuency-Extended GUG corpus (JFLEG) for developing and evaluating grammatical error correction (GEC). Unlike other corpora, it represents a broad range of language proficiency levels and uses holistic fluency edits to not only correct grammatical errors but also make the original text more native sounding. We describe the types of corrections made and bench...