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Incorporating Structural Alignment Biases into an Attentional Neural Translation Model., , , , , and . HLT-NAACL, page 876-885. The Association for Computational Linguistics, (2016)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)Unsupervised Recurrent Neural Network Grammars., , , , , and . NAACL-HLT (1), page 1105-1117. Association for Computational Linguistics, (2019)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)A Supertag-Context Model for Weakly-Supervised CCG Parser Learning., , , and . CoNLL, page 22-31. ACL, (2015)Generalizing and Hybridizing Count-based and Neural Language Models., and . EMNLP, page 1163-1172. The Association for Computational Linguistics, (2016)Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation., , , , , , , and . EMNLP, page 1520-1530. The Association for Computational Linguistics, (2015)Not All Contexts Are Created Equal: Better Word Representations with Variable Attention., , , , , , , and . EMNLP, page 1367-1372. The Association for Computational Linguistics, (2015)