Maria Nadejde

Also published as: Maria Nădejde


2019

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Enabling Robust Grammatical Error Correction in New Domains: Data Sets, Metrics, and Analyses
Courtney Napoles | Maria Nădejde | Joel Tetreault
Transactions of the Association for Computational Linguistics, Volume 7

Until now, grammatical error correction (GEC) has been primarily evaluated on text written by non-native English speakers, with a focus on student essays. This paper enables GEC development on text written by native speakers by providing a new data set and metric. We present a multiple-reference test corpus for GEC that includes 4,000 sentences in two new domains (formal and informal writing by native English speakers) and 2,000 sentences from a diverse set of non-native student writing. We also collect human judgments of several GEC systems on this new test set and perform a meta-evaluation, assessing how reliable automatic metrics are across these domains. We find that commonly used GEC metrics have inconsistent performance across domains, and therefore we propose a new ensemble metric that is robust on all three domains of text.

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Personalizing Grammatical Error Correction: Adaptation to Proficiency Level and L1
Maria Nadejde | Joel Tetreault
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)

Grammar error correction (GEC) systems have become ubiquitous in a variety of software applications, and have started to approach human-level performance for some datasets. However, very little is known about how to efficiently personalize these systems to the user’s characteristics, such as their proficiency level and first language, or to emerging domains of text. We present the first results on adapting a general purpose neural GEC system to both the proficiency level and the first language of a writer, using only a few thousand annotated sentences. Our study is the broadest of its kind, covering five proficiency levels and twelve different languages, and comparing three different adaptation scenarios: adapting to the proficiency level only, to the first language only, or to both aspects simultaneously. We show that tailoring to both scenarios achieves the largest performance improvement (3.6 F0.5) relative to a strong baseline.

2017

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Predicting Target Language CCG Supertags Improves Neural Machine Translation
Maria Nădejde | Siva Reddy | Rico Sennrich | Tomasz Dwojak | Marcin Junczys-Dowmunt | Philipp Koehn | Alexandra Birch
Proceedings of the Second Conference on Machine Translation

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Nematus: a Toolkit for Neural Machine Translation
Rico Sennrich | Orhan Firat | Kyunghyun Cho | Alexandra Birch | Barry Haddow | Julian Hitschler | Marcin Junczys-Dowmunt | Samuel Läubli | Antonio Valerio Miceli Barone | Jozef Mokry | Maria Nădejde
Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics

We present Nematus, a toolkit for Neural Machine Translation. The toolkit prioritizes high translation accuracy, usability, and extensibility. Nematus has been used to build top-performing submissions to shared translation tasks at WMT and IWSLT, and has been used to train systems for production environments.

2016

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Modeling Selectional Preferences of Verbs and Nouns in String-to-Tree Machine Translation
Maria Nădejde | Alexandra Birch | Philipp Koehn
Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers

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Edinburgh’s Statistical Machine Translation Systems for WMT16
Philip Williams | Rico Sennrich | Maria Nădejde | Matthias Huck | Barry Haddow | Ondřej Bojar
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

2015

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Edinburgh’s Syntax-Based Systems at WMT 2015
Philip Williams | Rico Sennrich | Maria Nadejde | Matthias Huck | Philipp Koehn
Proceedings of the Tenth Workshop on Statistical Machine Translation

2014

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EU-BRIDGE MT: Combined Machine Translation
Markus Freitag | Stephan Peitz | Joern Wuebker | Hermann Ney | Matthias Huck | Rico Sennrich | Nadir Durrani | Maria Nadejde | Philip Williams | Philipp Koehn | Teresa Herrmann | Eunah Cho | Alex Waibel
Proceedings of the Ninth Workshop on Statistical Machine Translation

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Edinburgh’s Syntax-Based Systems at WMT 2014
Philip Williams | Rico Sennrich | Maria Nadejde | Matthias Huck | Eva Hasler | Philipp Koehn
Proceedings of the Ninth Workshop on Statistical Machine Translation

2013

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The Feasibility of HMEANT as a Human MT Evaluation Metric
Alexandra Birch | Barry Haddow | Ulrich Germann | Maria Nadejde | Christian Buck | Philipp Koehn
Proceedings of the Eighth Workshop on Statistical Machine Translation

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Edinburgh’s Syntax-Based Machine Translation Systems
Maria Nadejde | Philip Williams | Philipp Koehn
Proceedings of the Eighth Workshop on Statistical Machine Translation