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.

本研究分析了神经机器翻译模型在不同粒度上学习到的表示，并通过相关的外围特性对其质量进行评估，结果表明深层次的 NMT 模型学习了大量的语言信息，其中鲜明的发现包括：（i）词组结构和词类等语言单元在模型较低的层次上被捕捉；（ii）而词汇语义或非局部的句法和语义依存则在较高层次上表示更好；（iii）使用字符所学习的表示比使用子词单位所学习的更具有词形信息；（iv）多语言模型所学习的表示比双语模型更加丰富。