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1
The language demographics of Amazon Mechanical Turk
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
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
In: http://www.cis.upenn.edu/~ccb/publications/language-demographics-of-mechanical-turk.pdf (2014)
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4
PPDB: The paraphrase database
In: http://aclweb.org/anthology-new/N/N13/N13-1092.pdf (2013)
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5
Dirt cheap web-scale parallel text from the common crawl
In: http://wing.comp.nus.edu.sg/~antho/P/P13/P13-1135.pdf (2013)
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6
Dirt cheap web-scale parallel text from the common crawl
In: http://www.cs.jhu.edu/~alopez/papers/acl2013-smith+etal.pdf (2013)
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7
Toward statistical machine translation without parallel corpora
In: http://www.cs.jhu.edu/~anni/papers/lowresmt/lowresmt.pdf (2012)
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8
Toward statistical machine translation without parallel corpora
In: http://people.mmci.uni-saarland.de/~aklement/publications/eacl12mt.pdf (2012)
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9
Constructing parallel corpora for six indian languages via crowdsourcing
In: http://www.aclweb.org/anthology/W12-3152/ (2012)
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10
Arabic dialect identification
In: http://www.aclweb.org/anthology/J/J14/J14-1006.pdf (2012)
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11
Machine translation of arabic dialects
In: http://www.aclweb.org/anthology-new/N/N12/N12-1006.pdf (2012)
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12
Learning sentential paraphrases from bilingual parallel corpora for text-to-text generation
In: http://www.cs.jhu.edu/~ccb/publications/learning-sentential-paraprhases-from-bilingual-parallel-corpora.pdf (2011)
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13
Incremental syntactic language models for phrase-based translation
In: http://wing.comp.nus.edu.sg/~antho/P/P11/P11-1063.pdf (2011)
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14
Incremental syntactic language models for phrase-based translation
In: http://www.cis.upenn.edu/~ccb/publications/incremental-syntactic-language-models-for-phrase-based-translation.pdf (2011)
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15
Crowdsourcing Translation: Professional Quality from Non-Professionals
In: http://cs.jhu.edu/%7Eozaidan/AOC/turk-trans_Zaidan-CCB_acl2011.pdf (2011)
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16
Paraphrastic sentence compression with a character-based metric: Tightening without deletion
In: http://www-csli.stanford.edu/%7Eccb/publications/paraphrastic-sentence-compression.pdf (2011)
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17
Paraphrastic sentence compression with a character-based metric: Tightening without deletion
In: http://aclweb.org/anthology-new/W/W11/W11-1610.pdf (2011)
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18
at
In: http://www-csli.stanford.edu/~ccb/publications/hiero-grammar-extraction-with-suffix-arrays.pdf (2010)
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19
Bilingual Lexicon Induction for Low-resource Languages
In: http://hltcoe.files.wordpress.com/2011/09/tr5bilinguallexicalinduction.pdf (2010)
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20
Moving Beyond Phrase Pairs: The Relevance of the Corpus in a SMT World
In: http://www.cs.cmu.edu/%7Eaphillip/publications/proposal.pdf (2010)
Abstract: Machine translation has advanced considerably in recent years, but primarily due to the availability of larger data sets. Translation of low-frequency phrases and resourcepoor languages is still a serious problem. In this work we explore a deeper integration of context, structure, and similarity within machine translation. Instead of modeling phrase pairs in abstract, we propose modeling each instance of a translation in the corpus. Unlike the traditional SMT approach that builds a mixture of independent, simple distributions for each phrase pair, our model is a mixture of translation instances. The significance lies in that we use a distance measure to assesses the relevance of each translation instance. It permits simple the integration of instance-specific features which we plan to exploit in three key directions. First, we will introduce non-local features that identify the relevant context of an instance in order to favor those that are most similar to the input. Second, we will mark-up the corpus with metadata from multiple external sources to sharpen the scoring of each translation instance and guide the overall translation process. Third, we will identify
URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.188.2673
http://www.cs.cmu.edu/%7Eaphillip/publications/proposal.pdf
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