  Neural machine translation (NMT) models generally adopt an encoder-decoder
architecture for modeling the entire translation process. The encoder
summarizes the representation of input sentence from scratch, which is
potentially a problem if the sentence is ambiguous. When translating a text,
humans often create an initial understanding of the source sentence and then
incrementally refine it along the translation on the target side. Starting from
this intuition, we propose a novel encoder-refiner-decoder framework, which
dynamically refines the source representations based on the generated
target-side information at each decoding step. Since the refining operations
are time-consuming, we propose a strategy, leveraging the power of
reinforcement learning models, to decide when to refine at specific decoding
steps. Experimental results on both Chinese-English and English-German
translation tasks show that the proposed approach significantly and
consistently improves translation performance over the standard encoder-decoder
framework. Furthermore, when refining strategy is applied, results still show
reasonable improvement over the baseline without much decrease in decoding
speed.
