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Using Ännotator Rationales" to Improve Machine Learning for Text Categorization.

, , and . HLT-NAACL, page 260-267. The Association for Computational Linguistics, (2007)

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Improving the Relevance of Product Search for Queries with Negations., , and . WWW (Companion Volume), page 86-89. ACM, (2023)Predicting Human-Targeted Translation Edit Rate via Untrained Human Annotators., and . HLT-NAACL, page 369-372. The Association for Computational Linguistics, (2010)Joshua 2.0: A Toolkit for Parsing-Based Machine Translation with Syntax, Semirings, Discriminative Training and Other Goodies., , , , , , , , , and 1 other author(s). WMT@ACL, page 133-137. Association for Computational Linguistics, (2010)Evaluating (and Improving) Sentence Alignment under Noisy Conditions., and . WMT@ACL, page 484-493. The Association for Computer Linguistics, (2013)Joshua: An Open Source Toolkit for Parsing-Based Machine Translation., , , , , , , and . WMT@EACL, page 135-139. Association for Computational Linguistics, (2009)Modeling Annotators: A Generative Approach to Learning from Annotator Rationales., and . EMNLP, page 31-40. ACL, (2008)Crowdsourcing Translation: Professional Quality from Non-Professionals., and . ACL, page 1220-1229. The Association for Computer Linguistics, (2011)An Enriched MT Grammar for Under $100., and . Mturk@HLT-NAACL, page 93-98. Association for Computational Linguistics, (2010)Using Ännotator Rationales" to Improve Machine Learning for Text Categorization., , and . HLT-NAACL, page 260-267. The Association for Computational Linguistics, (2007)Demonstration of Joshua: An Open Source Toolkit for Parsing-based Machine Translation., , , , , , , , and . ACL/IJCNLP (Software Demonstrations), page 25-28. The Association for Computer Linguistics, (2009)