Context-aware Natural Language Generation with Recurrent Neural Networks

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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.