What do Neural Machine Translation Models Learn about Morphology?
Neural machine translation (MT) models obtain state of the art performance while maintaining a simple, end to end architecture. However, little is known about what these models learn about source and target languages during the training process. In this work, we analyze the representations learned by neural MT models at various levels of granularity and empirically evaluate the quality of the representations for learning morphology through extrinsic part of speech and morphological tagging tasks. We conduct a thorough investigation along several parameters: word based vs. character based representations, depth of the encoding layer, the identity of the target language, and encoder vs. decoder representations. Our data driven, quantitative evaluation sheds light on important aspects in the neural MT system and its ability to capture word structure.
ACL