  Despite the recent success of deep neural networks in natural language
processing (NLP), their interpretability remains a challenge. We analyze the
representations learned by neural machine translation models at various levels
of granularity and evaluate their quality through relevant extrinsic
properties. In particular, we seek answers to the following questions: (i) How
accurately is word-structure captured within the learned representations, an
important aspect in translating morphologically-rich languages? (ii) Do the
representations capture long-range dependencies, and effectively handle
syntactically divergent languages? (iii) Do the representations capture lexical
semantics? We conduct a thorough investigation along several parameters: (i)
Which layers in the architecture capture each of these linguistic phenomena;
(ii) How does the choice of translation unit (word, character, or subword unit)
impact the linguistic properties captured by the underlying representations?
(iii) Do the encoder and decoder learn differently and independently? (iv) Do
the representations learned by multilingual NMT models capture the same amount
of linguistic information as their bilingual counterparts? Our data-driven,
quantitative evaluation illuminates important aspects in NMT models and their
ability to capture various linguistic phenomena. We show that deep NMT models
learn a non-trivial amount of linguistic information. Notable findings include:
i) Word morphology and part-of-speech information are captured at the lower
layers of the model; (ii) In contrast, lexical semantics or non-local syntactic
and semantic dependencies are better represented at the higher layers; (iii)
Representations learned using characters are more informed about wordmorphology
compared to those learned using subword units; and (iv) Representations learned
by multilingual models are richer compared to bilingual models.
