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.
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.
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.
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.
no code implementations • WS 2020 • Max White, Alla Rozovskaya
Grammatical Error Correction (GEC) is concerned with correcting grammatical errors in written text.
1 code implementation • WS 2019 • Michael Flor, Michael Fried, Alla Rozovskaya
We also develop a minimallysupervised context-aware approach to spelling correction.
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.
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.
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.
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.
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.