O
Omar F. Zaidan
Researcher at Johns Hopkins University
Publications - 27
Citations - 2600
Omar F. Zaidan is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: Machine translation & Task (project management). The author has an hindex of 17, co-authored 26 publications receiving 2400 citations. Previous affiliations of Omar F. Zaidan include Microsoft & St. Lawrence University.
Papers
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Proceedings Article
Crowdsourcing Translation: Professional Quality from Non-Professionals
TL;DR: A set of features that model both the translations and the translators, such as country of residence, LM perplexity of the translation, edit rate from the other translations, and (optionally) calibration against professional translators are proposed.
Proceedings Article
Using ``Annotator Rationales'' to Improve Machine Learning for Text Categorization
TL;DR: It is hypothesize that in some situations, providing rationales is a more fruitful use of an annotator's time than annotating more examples, and presents a learning method that exploits the rationales during training to boost performance significantly on a sample task, namely sentiment classification of movie reviews.
Journal ArticleDOI
Arabic dialect identification
TL;DR: This article describes the creation of a novel Arabic resource with dialect annotations, and uses the data to train and evaluate automatic classifiers for dialect identification, and establishes that classifiers using dialectal data significantly and dramatically outperform baselines that use MSA-only data, achieving near-human classification accuracy.
Journal Article
Findings of the 2011 Workshop on Statistical Machine Translation
TL;DR: The WMT11 shared tasks as mentioned in this paper included a translation task, a system combination task, and a task for machine translation evaluation metrics, and the results of these tasks were used to evaluate machine translation systems.
Proceedings Article
Findings of the 2010 Joint Workshop on Statistical Machine Translation and Metrics for Machine Translation
Chris Callison-Burch,Philipp Koehn,Christof Monz,Kay Peterson,Mark A. Przybocki,Omar F. Zaidan +5 more
TL;DR: A large-scale manual evaluation of 104 machine translation systems and 41 system combination entries was conducted, which used the ranking of these systems to measure how strongly automatic metrics correlate with human judgments of translation quality for 26 metrics.