This paper studied generating natural languages at particular contexts or
situations. We proposed two novel approaches which encode the contexts into a
continuous semantic representation and then decode the semantic representation
into text sequences with recurrent neural networks. During decoding, the
context information are attended through a gating mechanism, addressing the
problem of long-range dependency caused by lengthy sequences. We evaluate the
effectiveness of the proposed approaches on user review data, in which rich
contexts are available and two informative contexts, sentiments and products,
are selected for evaluation. Experiments show that the fake reviews generated
by our approaches are very natural. Results of fake review detection with human
judges show that more than 50\% of the fake reviews are misclassified as the
real reviews, and more than 90\% are misclassified by existing state-of-the-art
fake review detection algorithm.

本文研究了利用连续的语义表征通过递归神经网络生成特定情境下的自然语言的方法，提出了两种新方法，其中包含了注意力机制以解决因长序列引起的长程依赖问题，并在用户评论数据上进行了实验，展示了该方法生成的虚假评论非常自然，并且大部分能够躲过现有的虚假评论检测算法。