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.
Unsupervised methods for finding, analyzing, and controlling important neurons in NMT This paper presents unsupervised approaches to discovering important neurons in neural machine translation systems and analyzes linguistic properties controlled by those neurons. Unsupervised methods for ranking neurons in machine translation where important neurons are thus identified and used to control the MT output.