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Hits 1 – 20 of 21

1	The language demographics of Amazon Mechanical Turk
	Ellie Pavlick; Matt Post; Ann Irvine...
	In: https://tacl2013.cs.columbia.edu/ojs/index.php/tacl/article/viewFile/262/34/ (2014)
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2	The language demographics of Amazon Mechanical Turk. Transactions of the Association for Computational Linguistics
	Ellie Pavlick; Matt Post; Ann Irvine...
	In: http://www.seas.upenn.edu/%7Eepavlick/papers/language_demographics_mturk.pdf (2014)
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3	The language demographics of Amazon Mechanical Turk. Transactions of the Association for Computational Linguistics
	Ellie Pavlick; Matt Post; Ann Irvine...
	In: http://www.cis.upenn.edu/~ccb/publications/language-demographics-of-mechanical-turk.pdf (2014)
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4	Using Comparable Corpora to Augment Statistical Machine Translation Models in Low Resource Settings by
	Ann Irvine
	In: http://www.cs.jhu.edu/%7Eanni/irvineThesisJHU.pdf (2014)
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5	Sensespotting: Never let your parallel data tie you to an old domain
	Marine Carpuat; Hal Daumé Iii; Katharine Henry...
	In: http://www.umiacs.umd.edu/~hal/docs/daume13sensespotting.pdf (2013)
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6	Toward statistical machine translation without parallel corpora
	Alexandre Klementiev; Ann Irvine; Chris Callison-burch...
	In: http://www.cs.jhu.edu/~anni/papers/lowresmt/lowresmt.pdf (2012)
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7	Toward statistical machine translation without parallel corpora
	Alexandre Klementiev; Ann Irvine; Chris Callison-burch...
	In: http://people.mmci.uni-saarland.de/~aklement/publications/eacl12mt.pdf (2012)
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8	Bilingual Lexicon Induction for Low-resource Languages
	Alexandre Klementiev; Chris Callison-Burch; Ann Irvine
	In: http://hltcoe.files.wordpress.com/2011/09/tr5bilinguallexicalinduction.pdf (2010)
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9	Using mechanical turk to annotate lexicons for less commonly used languages
	Ann Irvine; Re Klementiev
	In: http://www.clsp.jhu.edu/%7Eanni/papers/irvine_lex_check.pdf (2010)
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10	Combining Bilingual and Comparable Corpora for Low Resource Machine Translation
	Ann Irvine; Chris Callison-burch
	In: http://wing.comp.nus.edu.sg/~antho/W/W13/W13-2233.pdf
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11	Combining Bilingual and Comparable Corpora for Low Resource Machine Translation
	Ann Irvine; Chris Callison-burch
	In: http://www.cs.jhu.edu/~ccb/publications/combining-bilingual-and-comparable-corpora.pdf
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12	Supervised Bilingual Lexicon Induction with Multiple Monolingual Signals
	Ann Irvine; Chris Callison-burch
	In: http://aclweb.org/anthology/N/N13/N13-1056.pdf
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13	Using Mechanical Turk to Annotate Lexicons for Less Commonly Used Languages
	Ann Irvine; Re Klementiev
	In: http://aclweb.org/anthology-new/W/W10/W10-0717.pdf
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14	Using Mechanical Turk to Annotate Lexicons for Less Commonly Used Languages
	Ann Irvine; Re Klementiev
	In: http://www.cs.jhu.edu/~anni/papers/irvine-klementiev-mturk-lexicons.pdf
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15	Combining Bilingual and Comparable Corpora for Low Resource Machine Translation
	Ann Irvine; Chris Callison-burch
	In: http://www.cs.jhu.edu/~anni/papers/irvineCCB_WMT13.pdf
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16	Toward Statistical Machine Translation without Parallel Corpora
	Alexandre Klementiev; Ann Irvine; Chris Callison-burch...
	In: http://cs.jhu.edu/%7Eccb/publications/toward-statistical-machine-translation-without-parallel-corpora.pdf
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17	Toward Statistical Machine Translation without Parallel Corpora
	Alexandre Klementiev; Ann Irvine; Chris Callison-burch...
	In: http://aclweb.org/anthology-new/E/E12/E12-1014.pdf
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18	Supervised Bilingual Lexicon Induction with Multiple Monolingual Signals
	Ann Irvine; Chris Callison-burch
	In: http://cs.jhu.edu/~ccb/publications/supervised-bilingual-lexicon-induction.pdf
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19	Hallucinating Phrase Translations for Low Resource MT
	Ann Irvine; Chris Callison-burch
	In: http://www.cs.jhu.edu/~anni/papers/irvineCCB_Hallucinating_CoNLL_14.pdf
	Abstract: We demonstrate that “hallucinating” phrasal translations can significantly improve the quality of machine translation in low resource conditions. Our hallucinated phrase tables consist of entries composed from multiple unigram translations drawn from the baseline phrase table and from translations that are induced from monolingual corpora. The hallucinated phrase table is very noisy. Its translations are low precision but high recall. We counter this by introducing 30 new feature functions (including a variety of monolinguallyestimated features) and by aggressively pruning the phrase table. Our analysis evaluates the intrinsic quality of our hallucinated phrase pairs as well as their impact in end-to-end Spanish-English and Hindi-English MT. 1
	URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.434.229 http://www.cs.jhu.edu/~anni/papers/irvineCCB_Hallucinating_CoNLL_14.pdf
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20	Hallucinating Phrase Translations for Low Resource MT
	Ann Irvine; Chris Callison-burch
	In: http://anthology.aclweb.org/W/W14/W14-1617.pdf
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