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Task-based Evaluation of Machine Translation (MT) Engines. Measuring How Well People Extract Who, When, Where-Type Elements in MT Output.

, and . EAMT, European Association for Machine Translation, (2006)

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On predicting social unrest using social media., , , , , , , , , and . ASONAM, page 89-95. IEEE Computer Society, (2016)Assessing Agreement in Human-Robot Dialogue Strategies: A Tale of Two Wizards., , , , , , , and . IVA, volume 10011 of Lecture Notes in Computer Science, page 484-488. (2016)Training and Evaluating a Statistical Part of Speech Tagger for Natural Language Applications using Kepler Workflows., , , , and . ICCS, volume 9 of Procedia Computer Science, page 1588-1594. Elsevier, (2012)Exploitation of an Arabic Language Resource for Machine Translation Evaluation: using Buckwalter-based Lookup Tool to Augment CMU Alignment Algorithm., , and . LREC, European Language Resources Association, (2008)Symposium report.. Mach. Transl., 8 (3): 203-207 (1993)Special Issue on Embedded MT Systems; Overview., and . Mach. Transl., 17 (4): 307-319 (2002)Object and Text-guided Semantics for CNN-based Activity Recognition., , , , and . ICASSP, page 1458-1462. IEEE, (2019)Task-based Evaluation of Machine Translation (MT) Engines. Measuring How Well People Extract Who, When, Where-Type Elements in MT Output., and . EAMT, European Association for Machine Translation, (2006)Combining Evaluation Metrics via Loss Functions., and . AMTA, page 242-250. Association for Machine Translation in the Americas, (2006)Buckwalter-based Lookup Tool as Language Resource for Arabic Language Learners., and . SETQALNLP, page 66-67. Association for Computational Linguistics, (2008)