Grammatical error diagnosis is an essential part in a language-learning tutoring system. Based on the data sets of Chinese grammar error detection tasks, we proposed a system which measures the likelihood of correction candidates generated by deleting or inserting characters or words, moving substrings to different positions, substituting prepositions with other prepositions, or substituting wo...

This paper describes our system in the shared task of CoNLL-2013. We illustrate that grammatical error detection and correction can be transformed into a multiclass classification task and implemented as a single-model system regardless of various error types with the aid of maximum entropy modeling. Our system achieves the F1 score of 17.13% on the standard test set.

The goal of our research is to build a grammatical error-tagged corpus for Korean learners of Spoken English dubbed Postech Learner Corpus. We collected raw story-telling speech from Korean university students. Transcription and annotation using the Cambridge Learner Corpus tagset were performed by six Korean annotators fluent in English. For the annotation of the corpus, we developed an annota...

This paper describes our submission for the CoNLL 2013 Shared Task, which aims to to improve the detection and correction of the five most common grammatical error types in English text written by non-native speakers. Our system concentrates only on two of them; it employs machine learning classifiers for the ArtOrDet-, and a fully deterministic rule based workflow for the SVA error type.

We present an approach to grammatical error correction for the CoNLL 2013 shared task based on a weighted tree-to-string transducer. Rules for the transducer are extracted from the NUCLE training data. An n-gram language model is used to rerank k-best sentence lists generated by the transducer. Our system obtains a precision, recall and F1 score of 0.27, 0.1333 and 0.1785, respectively, on the ...

We build a grammatical error correction (GEC) system primarily based on the state-of-the-art statistical machine translation (SMT) approach, using task-specific features and tuning, and further enhance it with the modeling power of neural network joint models. The SMT-based system is weak in generalizing beyond patterns seen during training and lacks granularity below the word level. To address...

The CoNLL-2013 shared task was devoted to grammatical error correction. In this paper, we give the task definition, present the data sets, and describe the evaluation metric and scorer used in the shared task. We also give an overview of the various approaches adopted by the participating teams, and present the evaluation results.

This study develops a sentence judgment system using both rule-based and n-gram statistical methods to detect grammatical errors in Chinese sentences. The rule-based method provides 142 rules developed by linguistic experts to identify potential rule violations in input sentences. The n-gram statistical method relies on the n-gram scores of both correct and incorrect training sentences to deter...

We demonstrate that an attention-based encoder-decoder model can be used for sentence-level grammatical error identification for the Automated Evaluation of Scientific Writing (AESW) Shared Task 2016. The attention-based encoder-decoder models can be used for the generation of corrections, in addition to error identification, which is of interest for certain end-user applications. We show that ...

This paper explores the issue of automatically generated ungrammatical data and its use in error detection, with a focus on the task of classifying a sentence as grammatical or ungrammatical. We present an error generation tool called GenERRate and show how GenERRate can be used to improve the performance of a classifier on learner data. We describe initial attempts to replicate Cambridge Learn...