RESEARCH

NLP

Jump to better conclusions: SCAN both left and right

November 02, 2018

Abstract

Lake and Baroni (2018) recently introduced the SCAN data set, which consists of simple commands paired with action sequences and is intended to test the strong generalization abilities of recurrent sequence-to-sequence models. Their initial experiments suggested that such models may fail because they lack the ability to extract systematic rules. Here, we take a closer look at SCAN and show that it does not always capture the kind of generalization that it was designed for. To mitigate this we propose a complementary dataset, which requires mapping actions back to the original commands, called NACS. We show that models that do well on SCAN do not necessarily do well on NACS, and that NACS exhibits properties more closely aligned with realistic usecases for sequence-to-sequence models.

Download the Paper

AUTHORS

Written by

Douwe Kiela

Jason Weston

Kyunghyun Cho

Marco Baroni

Joost Bastings

Publisher

Workshop on Analyzing and Interpreting Neural Networks for NLP

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