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From Characters to Understanding Natural Language (C2NLU): Robust End-to-End Deep Learning for NLP (Dagstuhl Seminar 17042).

, , , and . Dagstuhl Reports, 7 (1): 129-157 (2017)

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The CMU Machine Translation Systems at WMT 2013: Syntax, Synthetic Translation Options, and Pseudo-References., , , , , , , , , and . WMT@ACL, page 70-77. The Association for Computer Linguistics, (2013)Fast, Easy, and Cheap: Construction of Statistical Machine Translation Models with MapReduce., , , and . WMT@ACL, page 199-207. Association for Computational Linguistics, (2008)Crowdsourcing High-Quality Parallel Data Extraction from Twitter., , , , and . WMT@ACL, page 426-436. The Association for Computer Linguistics, (2014)Attention-based Multimodal Neural Machine Translation., , , , and . WMT, page 639-645. The Association for Computer Linguistics, (2016)Incorporating Structural Alignment Biases into an Attentional Neural Translation Model., , , , , and . HLT-NAACL, page 876-885. The Association for Computational Linguistics, (2016)Ontologically Grounded Multi-sense Representation Learning for Semantic Vector Space Models., , and . HLT-NAACL, page 683-693. The Association for Computational Linguistics, (2015)Unsupervised POS Induction with Word Embeddings., , , and . HLT-NAACL, page 1311-1316. The Association for Computational Linguistics, (2015)Unsupervised Recurrent Neural Network Grammars., , , , , and . NAACL-HLT (1), page 1105-1117. Association for Computational Linguistics, (2019)A Simple, Fast, and Effective Reparameterization of IBM Model 2., , and . HLT-NAACL, page 644-648. The Association for Computational Linguistics, (2013)Supersense Tagging for Arabic: the MT-in-the-Middle Attack., , , , and . HLT-NAACL, page 661-667. The Association for Computational Linguistics, (2013)