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Identifying and Controlling Important Neurons in Neural Machine Translation
lib:4812b6a83034a3e0 (v1.0.0)
Authors: Anthony Bau,Yonatan Belinkov,Hassan Sajjad,Nadir Durrani,Fahim Dalvi,James Glass
Where published: ICLR 2019 5
ArXiv: 1811.01157
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1811.01157v1
Neural machine translation (NMT) models learn representations containing substantial linguistic information. However, it is not clear if such information is fully distributed or if some of it can be attributed to individual neurons. We develop unsupervised methods for discovering important neurons in NMT models. Our methods rely on the intuition that different models learn similar properties, and do not require any costly external supervision. We show experimentally that translation quality depends on the discovered neurons, and find that many of them capture common linguistic phenomena. Finally, we show how to control NMT translations in predictable ways, by modifying activations of individual neurons.
Where published: ICLR 2019 5
ArXiv: 1811.01157
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1811.01157v1
Neural machine translation (NMT) models learn representations containing substantial linguistic information. However, it is not clear if such information is fully distributed or if some of it can be attributed to individual neurons. We develop unsupervised methods for discovering important neurons in NMT models. Our methods rely on the intuition that different models learn similar properties, and do not require any costly external supervision. We show experimentally that translation quality depends on the discovered neurons, and find that many of them capture common linguistic phenomena. Finally, we show how to control NMT translations in predictable ways, by modifying activations of individual neurons.
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