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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 6 of 6 1 Using Comparable Corpora to Augment Statistical Machine Translation Models in Low Resource Settings Irvine, Ann. - 2014 BASE Show details 2 Use of Modality and Negation in Semantically-Informed Syntactic MT Bloodgood, Michael; Filardo, Nathaniel; Levin, Lori. - : MIT Press, 2012 BASE Show details 3 Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach ... Baker, Kathryn; Bloodgood, Michael; Callison-Burch, Chris. - : Digital Repository at the University of Maryland, 2010 BASE Show details 4 Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach Baker, Kathryn; Piatko, Christine; Miller, Scott. - 2010 BASE Show details 5 Using Mechanical Turk to Build Machine Translation Evaluation Sets Bloodgood, Michael; Callison-Burch, Chris. - : Association for Computational Linguistics, 2010 BASE Show details 6 Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation Bloodgood, Michael; Callison-Burch, Chris. - : Association for Computational Linguistics, 2010 Abstract: We explore how to improve machine translation systems by adding more translation data in situations where we already have substantial resources. The main challenge is how to buck the trend of diminishing returns that is commonly encountered. We present an active learning-style data solicitation algorithm to meet this challenge. We test it, gathering annotations via Amazon Mechanical Turk, and find that we get an order of magnitude increase in performance rates of improvement. ; Johns Hopkins University Human Language Technology Center of Excellence Keyword: active learning; Amazon Mechanical Turk; annotation bottleneck; annotation cost metrics; annotation costs; artificial intelligence; computational linguistics; computer science; cost-efficient annotation; cost-focused active learning; crowdsourcing; Highlighted N-Gram Method; human language technology; machine learning; machine translation; natural language processing; query learning; selective sampling; statistical machine translation; statistical methods; stopping criteria; stopping methods; translation technology; uncertainty-based active learning; uncertainty-based sampling; Urdu-English translation URL: http://hdl.handle.net/1903/15549 BASE Hide details

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