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The Fast and the Flexible: Training Neural Networks to Learn to Follow Instructions from Small Data.

, , , and . IWCS (1), page 223-234. Association for Computational Linguistics, (2019)

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The Fast and the Flexible: Training Neural Networks to Learn to Follow Instructions from Small Data., , , and . IWCS (1), page 223-234. Association for Computational Linguistics, (2019)CommAI: Evaluating the first steps towards a useful general AI., , , , , , and . ICLR (Workshop), OpenReview.net, (2017)Don't count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors., , and . ACL (1), page 238-247. The Association for Computer Linguistics, (2014)Memorize or generalize? Searching for a compositional RNN in a haystack., , and . CoRR, (2018)Learning compositionally through attentive guidance., , , , and . CoRR, (2018)The Fast and the Flexible: training neural networks to learn to follow instructions from small data., , , and . CoRR, (2018)The emergence of number and syntax units in LSTM language models., , , , , and . NAACL-HLT (1), page 11-20. Association for Computational Linguistics, (2019)There Is No Logical Negation Here, But There Are Alternatives: Modeling Conversational Negation with Distributional Semantics., , , and . Computational Linguistics, 42 (4): 637-660 (2016)How Cosmopolitan Are Emojis?: Exploring Emojis Usage and Meaning over Different Languages with Distributional Semantics., , , and . ACM Multimedia, page 531-535. ACM, (2016)Dead parrots make bad pets: Exploring modifier effects in noun phrases., and . *SEM@COLING, page 171-181. The *SEM 2014 Organizing Committee, (2014)