1,180 research outputs found
Compositional sequence labeling models for error detection in learner writing
© 2016 Association for Computational Linguistics. In this paper, we present the first experiments using neural network models for the task of error detection in learner writing. We perform a systematic comparison of alternative compositional architectures and propose a framework for error detection based on bidirectional LSTMs. Experiments on the CoNLL-14 shared task dataset show the model is able to outperform other participants on detecting errors in learner writing. Finally, the model is integrated with a publicly deployed self-assessment system, leading to performance comparable to human annotators
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Correction Detection and Error Type Selection as an ESL Educational Aid
We present a classifier that discriminates between types of corrections made by teachers of English in student essays. We define a set of linguistically motivated feature templates for a log-linear classification model, train this classifier on sentence pairs extracted from the Cambridge Learner Corpus, and achieve 89% accuracy improving upon a 33% baseline. Furthermore, we incorporate our classifier into a novel application that takes as input a set of corrected essays that have been sentence aligned with their originals and outputs the individual corrections classified by error type. We report the F-Score of our implementation on this task.Engineering and Applied Science
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Neural Sequence-Labelling Models for Grammatical Error Correction
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 approach
achieves state-of-the-art results on error
correction for three different datasets, and
it has the additional advantage of only using
a small set of easily computed features
that require no linguistic input
Grammatical error correction using hybrid systems and type filtering
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 achieves state of-the-art results, ranking first on the original test set and second on the test set with alternative annotations.[We would like to thank] Cambridge English Language Assessment, a division of Cambridge Assessment, for supporting this research
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