We propose a model for Chinese poem generation based on recurrent neural net- works which we argue is ideally suited to capturing poetic content and form. Our generator jointly performs content selec- tion (?what to say?) and surface realization (?how to say?) by learning representations of individual characters, and their com- binations into one or more lines as well as how these mutually reinforce and con- strain each other. Poem lines are gener- ated incrementally by taking into account the entire history of what has been gen- erated so far rather than the limited hori- zon imposed by the previous line or lexical n-grams. Experimental results show that our model outperforms competitive Chi- nese poetry generation systems using both automatic and manual evaluation methods.

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