Grammatical Error Detection
17 papers with code • 4 benchmarks • 4 datasets
Grammatical Error Detection (GED) is the task of detecting different kinds of errors in text such as spelling, punctuation, grammatical, and word choice errors. Grammatical error detection (GED) is one of the key component in grammatical error correction (GEC) community.
Benchmarks
These leaderboards are used to track progress in Grammatical Error Detection
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CoNLL
JFLEG
FCGEC
Latest papers with no code
Multi-Head Multi-Layer Attention to Deep Language Representations for Grammatical Error Detection
In this work, we investigate the effect of utilizing information not only from the final layer but also from intermediate layers of a pre-trained language representation model to detect grammatical errors.
A Hybrid Approach Combining Statistical Knowledge with Conditional Random Fields for Chinese Grammatical Error Detection
This paper presents a method of combining Conditional Random Fields (CRFs) model with a post-processing layer using Google n-grams statistical information tailored to detect word selection and word order errors made by learners of Chinese as Foreign Language (CFL).
Neural sequence modelling for learner error prediction
This paper describes our use of two recurrent neural network sequence models: sequence labelling and sequence-to-sequence models, for the prediction of future learner errors in our submission to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM).
Neural Multi-task Learning in Automated Assessment
Grammatical error detection and automated essay scoring are two tasks in the area of automated assessment.
Collecting fluency corrections for spoken learner English
We present crowdsourced collection of error annotations for transcriptions of spoken learner English.
Artificial Error Generation with Machine Translation and Syntactic Patterns
Shortage of available training data is holding back progress in the area of automated error detection.
Auxiliary Objectives for Neural Error Detection Models
We investigate the utility of different auxiliary objectives and training strategies within a neural sequence labeling approach to error detection in learner writing.
Detection of Chinese Word Usage Errors for Non-Native Chinese Learners with Bidirectional LSTM
Selecting appropriate words to compose a sentence is one common problem faced by non-native Chinese learners.
