April 25, 2020
Humans understand novel sentences by composing meanings and roles of core language components. In contrast, neural network models for natural language modeling fail when such compositional generalization is required. The main contribution of this paper is to hypothesize that language compositionality is a form of group-equivariance. Based on this hypothesis, we propose a set of tools for constructing equivariant sequence-to-sequence models. Throughout a variety of experiments on the SCAN tasks, we analyze the behavior of existing models under the lens of equivariance, and demonstrate that our equivariant architecture is able to achieve the type compositional generalization required in human language understanding.
June 14, 2020
Ronghang Hu, Amanpreet Singh, Trevor Darrell, Marcus Rohrbach
June 14, 2020
April 25, 2020
Jonathan Gordon, David Lopez-Paz, Marco Baroni, Diane Bouchacourt
April 25, 2020
September 15, 2019
Tatiana Likhomanenko, Gabriel Synnaeve, Ronan Collobert
September 15, 2019
September 10, 2019
Jinfeng Rao, Linqing Liu, Yi Tay, Wei Yang, Peng Shi, Jimmy Lin
September 10, 2019
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