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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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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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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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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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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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Use of Modality and Negation in Semantically-Informed Syntactic MT ...
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Fisher and CALLHOME Spanish--English Speech Translation
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Abstract:
*Introduction* Fisher and CALLHOME Spanish-English Speech Translation was developed at Johns Hopkins University and contains English reference translations and speech recognizer output (in various forms) that complement the LDC Fisher Spanish (LDC2010T04) and CALLHOME Spanish audio and transcript releases (LDC96T17). Together, they make a four-way parallel text dataset representing approximately 38 hours of speech, with defined training, development, and held-out test sets. *Data* The source data are the Fisher Spanish and CALLOME Spanish corpora developed by LDC, comprising transcribed telephone conversations between (mostly native) Spanish speakers in a variety of dialects. The Fisher Spanish data set consists of 819 transcribed conversations on an assortment of provided topics primarily between strangers, resulting in approximately 160 hours of speech aligned at the utterance level, with 1.5 million tokens. The CALLHOME Spanish corpus comprises 120 transcripts of spontaneous conversations primarily between friends and family members, resulting in approximately 20 hours of speech aligned at the utterance level, with just over 200,000 words (tokens) of transcribed text. Translations were obtained by crowdsourcing using Amazon's Mechanical Turk, after which the data was split into training, development, and test sets. The CALLHOME data set defines its own data splits, organized into train, devtest, and evltest, which were retained here. For the Fisher material, four data splits were produced: a large training section and three test sets. These test sets correspond to portions of the data where four translations exist. *Samples* Please view this corpus and mapping sample. *Updates* None at this time.
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URL: https://catalog.ldc.upenn.edu/LDC2014T23
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FEATURE-DRIVEN QUESTION ANSWERING WITH NATURAL LANGUAGE ALIGNMENT
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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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Fisher and CALLHOME Spanish--English Speech Translation ...
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Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach ...
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Dirt cheap web-scale parallel text from the Common Crawl ...
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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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Use of Modality and Negation in Semantically-Informed Syntactic MT
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In: DTIC (2012)
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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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