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Simplification Using Paraphrases and Context-Based Lexical Substitution
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In: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies ; https://hal.archives-ouvertes.fr/hal-01838519 ; Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, Jun 2018, Nouvelle Orléans, United States (2018)
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| 22 |
Automated Paraphrase Lattice Creation for HyTER Machine Translation Evaluation
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In: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies ; https://hal.archives-ouvertes.fr/hal-01838521 ; Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics Jun 2018, Nouvelle Orléans, United States (2018)
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Comparing Constraints for Taxonomic Organization
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In: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies ; https://hal.archives-ouvertes.fr/hal-01838520 ; Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics Jun 2018, Nouvelle Orléans, United States (2018)
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| 24 |
Mapping the Paraphrase Database to WordNet
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In: Conference on Lexical and Computational Semantics ; https://hal.archives-ouvertes.fr/hal-01838527 ; Conference on Lexical and Computational Semantics, Aug 2017, Vancouver, Canada (2017)
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Learning Antonyms with Paraphrases and a Morphology-aware Neural Network
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In: Conference on Lexical and Computational Semantics ; https://hal.archives-ouvertes.fr/hal-01838526 ; Conference on Lexical and Computational Semantics, Aug 2017, Vancouver, Canada (2017)
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Word Sense Filtering Improves Embedding-Based Lexical Substitution
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In: Conference of the European Chapter of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-01838524 ; Conference of the European Chapter of the Association for Computational Linguistics , Apr 2017, Valencia, Spain (2017)
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| 27 |
Learning Translations via Matrix Completion
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In: Conference on Empirical Methods in Natural Language Processing ; https://hal.archives-ouvertes.fr/hal-01838532 ; Conference on Empirical Methods in Natural Language Processing, Sep 2017, Copenhagen, Denmark (2017)
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| 28 |
KnowYourNyms? A Game of Semantic Relationships
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In: Conference on Empirical Methods in Natural Language Processing ; https://hal.archives-ouvertes.fr/hal-01838528 ; Conference on Empirical Methods in Natural Language Processing, Sep 2017, Copenhagen, Denmark (2017)
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| 29 |
Use of Modality and Negation in Semantically-Informed Syntactic MT ...
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| 31 |
FEATURE-DRIVEN QUESTION ANSWERING WITH NATURAL LANGUAGE ALIGNMENT
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| 32 |
Using Comparable Corpora to Augment Statistical Machine Translation Models in Low Resource Settings
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Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation ...
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| 34 |
Fisher and CALLHOME Spanish--English Speech Translation ...
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Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach ...
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| 36 |
Dirt cheap web-scale parallel text from the Common Crawl ...
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| 37 |
Dirt cheap web-scale parallel text from the Common Crawl
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In: Smith, Jason R; Saint-Amand, Herve; Plamada, Magdalena; Koehn, Philipp; Callison-Burch, Chris; Lopez, Adam (2013). Dirt cheap web-scale parallel text from the Common Crawl. In: 51st Annual Meeting of the Association for Computational Linguistics, Sofia, Bulgaria, August 2013. Association for Computational Linguistics, 1374-1383. (2013)
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Abstract:
Parallel text is the fuel that drives modern machine translation systems. The Web is a comprehensive source of preexisting parallel text, but crawling the entire web is impossible for all but the largest companies. We bring web-scale parallel text to the masses by mining the Common Crawl, a public Web crawl hosted on Amazon 19s Elastic Cloud. Starting from nothing more than a set of common two-letter language codes, our open-source extension of the STRAND algorithm mined 32 terabytes of the crawl in just under a day, at a cost of about \$500. Our large-scale experiment uncovers large amounts of parallel text in dozens of language pairs across a variety of domains and genres, some previously unavailable in curated datasets. Even with minimal cleaning and filtering, the resulting data boosts translation performance across the board for five different language pairs in the news domain, and on open domain test sets we see improvements of up to 5 BLEU. We make our code and data available for other researchers seeking to mine this rich new data resource.
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Keyword:
000 Computer science; 410 Linguistics; Institute of Computational Linguistics; knowledge & systems
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URL: https://www.zora.uzh.ch/id/eprint/80038/1/ACL2013135.pdf
https://doi.org/10.5167/uzh-80038
https://www.zora.uzh.ch/id/eprint/80038/
http://www.aclweb.org/anthology/P13-1135
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| 38 |
Use of Modality and Negation in Semantically-Informed Syntactic MT
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In: DTIC (2012)
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| 39 |
Use of Modality and Negation in Semantically-Informed Syntactic MT
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Incremental Syntactic Language Models for Phrase-Based Translation
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In: DTIC (2011)
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