  Neural machine translation (NMT) typically adopts the encoder-decoder
framework. A good understanding of the characteristics and functionalities of
the encoder and decoder can help to explain the pros and cons of the framework,
and design better models for NMT. In this work, we conduct an empirical study
on the encoder and the decoder in NMT, taking Transformer as an example. We
find that 1) the decoder handles an easier task than the encoder in NMT, 2) the
decoder is more sensitive to the input noise than the encoder, and 3) the
preceding words/tokens in the decoder provide strong conditional information,
which accounts for the two observations above. We hope those observations can
shed light on the characteristics of the encoder and decoder and inspire future
research on NMT.
