Search Results for author:
Found 25 papers, 1 papers with code
Automatic Generation of Distractors for Fill-in-the-Blank Exercises with Round-Trip Neural Machine Translation
In a fill-in-the-blank exercise, a student is presented with a carrier sentence with one word hidden, and a multiple-choice list that includes the correct answer and several inappropriate options, called distractors.
Automatic Classification of Russian Learner Errors
Grammatical Error Correction systems are typically evaluated overall, without taking into consideration performance on individual error types because system output is not annotated with respect to error type.
Spelling Correction for Russian: A Comparative Study of Datasets and Methods
We develop a minimally-supervised model for spelling correction and evaluate its performance on three datasets annotated for spelling errors in Russian.
How Good (really) are Grammatical Error Correction Systems?
Standard evaluations of Grammatical Error Correction (GEC) systems make use of a fixed reference text generated relative to the original text; they show, even when using multiple references, that we have a long way to go.
A Comparative Study of Synthetic Data Generation Methods for Grammatical Error Correction
Grammatical Error Correction (GEC) is concerned with correcting grammatical errors in written text.
A Benchmark Corpus of English Misspellings and a Minimally-supervised Model for Spelling Correction
We also develop a minimallysupervised context-aware approach to spelling correction.
Grammar Error Correction in Morphologically Rich Languages: The Case of Russian
Although impressive results have recently been achieved for grammar error correction of non-native English writing, these results are limited to domains where plentiful training data are available.
Predicting Discharge Disposition Using Patient Complaint Notes in Electronic Medical Records
We use this corpus to develop a model that uses the complaint and diagnosis information to predict patient disposition.
Adapting to Learner Errors with Minimal Supervision
This article considers the problem of correcting errors made by English as a Second Language writers from a machine learning perspective, and addresses an important issue of developing an appropriate training paradigm for the task, one that accounts for error patterns of non-native writers using minimal supervision.
Large Scale Arabic Error Annotation: Guidelines and Framework
Finally, we present the annotation tool that was developed as part of this project, the annotation pipeline, and the quality of the resulting annotations.
Building a State-of-the-Art Grammatical Error Correction System
This paper identifies and examines the key principles underlying building a state-of-the-art grammatical error correction system.
