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Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning.

Y. Tsvetkov, M. Faruqui, W. Ling, B. MacWhinney, and C. Dyer. ACL (1), The Association for Computer Linguistics, (2016)

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Morphological Inflection Generation Using Character Sequence to Sequence Learning.M. Faruqui, Y. Tsvetkov, G. Neubig, and C. Dyer. HLT-NAACL, page 634-643. The Association for Computational Linguistics, (2016)Revisiting the Boundary between ASR and NLU in the Age of Conversational Dialog Systems.M. Faruqui, and D. Hakkani-Tür. Comput. Linguistics, 48 (1): 221-232 (2022)Problems With Evaluation of Word Embeddings Using Word Similarity Tasks.M. Faruqui, Y. Tsvetkov, P. Rastogi, and C. Dyer. RepEval@ACL, page 30-35. Association for Computational Linguistics, (2016)Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering.A. Gupta, J. Xu, S. Upadhyay, D. Yang, and M. Faruqui. ACL/IJCNLP (Findings), volume ACL/IJCNLP 2021 of Findings of ACL, page 3309-3319. Association for Computational Linguistics, (2021)An Information Theoretic Approach to Bilingual Word Clustering.M. Faruqui, and C. Dyer. ACL (2), page 777-783. The Association for Computer Linguistics, (2013)Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention.T. Munkhdalai, M. Faruqui, and S. Gopal. CoRR, (2024)Evaluation of Word Vector Representations by Subspace Alignment.Y. Tsvetkov, M. Faruqui, W. Ling, G. Lample, and C. Dyer. EMNLP, page 2049-2054. The Association for Computational Linguistics, (2015)ToTTo: A Controlled Table-To-Text Generation Dataset.A. Parikh, X. Wang, S. Gehrmann, M. Faruqui, B. Dhingra, D. Yang, and D. Das. EMNLP (1), page 1173-1186. Association for Computational Linguistics, (2020)Learning Word Representations with Hierarchical Sparse Coding.D. Yogatama, M. Faruqui, C. Dyer, and N. Smith. CoRR, (2014)Morpho-syntactic Lexicon Generation Using Graph-based Semi-supervised Learning.M. Faruqui, R. McDonald, and R. Soricut. CoRR, (2015)

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Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning.

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Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning.