  Unsupervised style transfer models are mainly based on an inductive learning
approach, which represents the style as embeddings, decoder parameters, or
discriminator parameters and directly applies these general rules to the test
cases. However, the lacking of parallel corpus hinders the ability of these
inductive learning methods on this task. As a result, it is likely to cause
severe inconsistent style expressions, like `the salad is rude`. To tackle this
problem, we propose a novel transductive learning approach in this paper, based
on a retrieval-based context-aware style representation. Specifically, an
attentional encoder-decoder with a retriever framework is utilized. It involves
top-K relevant sentences in the target style in the transfer process. In this
way, we can learn a context-aware style embedding to alleviate the above
inconsistency problem. In this paper, both sparse (BM25) and dense retrieval
functions (MIPS) are used, and two objective functions are designed to
facilitate joint learning. Experimental results show that our method
outperforms several strong baselines. The proposed transductive learning
approach is general and effective to the task of unsupervised style transfer,
and we will apply it to the other two typical methods in the future.
