Search Results for author: Alla Rozovskaya

Found 26 papers, 2 papers with code

Automatic Classification of Russian Learner Errors

no code implementations LREC 2022 Alla Rozovskaya

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.

Classification Grammatical Error Correction

Automatic Generation of Distractors for Fill-in-the-Blank Exercises with Round-Trip Neural Machine Translation

no code implementations ACL 2022 Subhadarshi Panda, Frank Palma Gomez, Michael Flor, Alla Rozovskaya

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.

Machine Translation Multiple-choice +2

Spelling Correction for Russian: A Comparative Study of Datasets and Methods

no code implementations RANLP 2021 Alla Rozovskaya

We develop a minimally-supervised model for spelling correction and evaluate its performance on three datasets annotated for spelling errors in Russian.

Cross-corpus Machine Translation +3

How Good (really) are Grammatical Error Correction Systems?

no code implementations EACL 2021 Alla Rozovskaya, Dan Roth

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.

Grammatical Error Correction

Grammar Error Correction in Morphologically Rich Languages: The Case of Russian

no code implementations TACL 2019 Alla Rozovskaya, Dan Roth

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

no code implementations WS 2018 Mohamad Salimi, Alla Rozovskaya

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

no code implementations CL 2017 Alla Rozovskaya, Dan Roth, Mark Sammons

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

no code implementations LREC 2014 Wajdi Zaghouani, Behrang Mohit, Nizar Habash, Ossama Obeid, Nadi Tomeh, Alla Rozovskaya, Noura Farra, Sarah Alkuhlani, Kemal Oflazer

Finally, we present the annotation tool that was developed as part of this project, the annotation pipeline, and the quality of the resulting annotations.

Machine Translation

Building a State-of-the-Art Grammatical Error Correction System

no code implementations TACL 2014 Alla Rozovskaya, Dan Roth

This paper identifies and examines the key principles underlying building a state-of-the-art grammatical error correction system.

Grammatical Error Correction Machine Translation

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