Olivia Buzek


Learning to translate with products of novices: a suite of open-ended challenge problems for teaching MT
Adam Lopez | Matt Post | Chris Callison-Burch | Jonathan Weese | Juri Ganitkevitch | Narges Ahmidi | Olivia Buzek | Leah Hanson | Beenish Jamil | Matthias Lee | Ya-Ting Lin | Henry Pao | Fatima Rivera | Leili Shahriyari | Debu Sinha | Adam Teichert | Stephen Wampler | Michael Weinberger | Daguang Xu | Lin Yang | Shang Zhao
Transactions of the Association for Computational Linguistics, Volume 1

Machine translation (MT) draws from several different disciplines, making it a complex subject to teach. There are excellent pedagogical texts, but problems in MT and current algorithms for solving them are best learned by doing. As a centerpiece of our MT course, we devised a series of open-ended challenges for students in which the goal was to improve performance on carefully constrained instances of four key MT tasks: alignment, decoding, evaluation, and reranking. Students brought a diverse set of techniques to the problems, including some novel solutions which performed remarkably well. A surprising and exciting outcome was that student solutions or their combinations fared competitively on some tasks, demonstrating that even newcomers to the field can help improve the state-of-the-art on hard NLP problems while simultaneously learning a great deal. The problems, baseline code, and results are freely available.


The Value of Monolingual Crowdsourcing in a Real-World Translation Scenario: Simulation using Haitian Creole Emergency SMS Messages
Chang Hu | Philip Resnik | Yakov Kronrod | Vladimir Eidelman | Olivia Buzek | Benjamin B. Bederson
Proceedings of the Sixth Workshop on Statistical Machine Translation


Crowdsourcing the evaluation of a domain-adapted named entity recognition system
Asad B. Sayeed | Timothy J. Meyer | Hieu C. Nguyen | Olivia Buzek | Amy Weinberg
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

Improving Translation via Targeted Paraphrasing
Philip Resnik | Olivia Buzek | Chang Hu | Yakov Kronrod | Alex Quinn | Benjamin B. Bederson
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

Error Driven Paraphrase Annotation using Mechanical Turk
Olivia Buzek | Philip Resnik | Ben Bederson
Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk

Position Paper: Improving Translation via Targeted Paraphrasing
Yakov Kronrod | Philip Resnik | Olivia Buzek | Chang Hu | Alex Quinn | Ben Bederson
Proceedings of the Workshop on Collaborative Translation: technology, crowdsourcing, and the translator perspective

Targeted paraphrasing is a new approach to the problem of obtaining cost-effective, reasonable quality translation that makes use of simple and inexpensive human computations by monolingual speakers in combination with machine translation. The key insight behind the process is that it is possible to spot likely translation errors with only monolingual knowledge of the target language, and it is possible to generate alternative ways to say the same thing (i.e. paraphrases) with only monolingual knowledge of the source language. Evaluations demonstrate that this approach can yield substantial improvements in translation quality.