Neural network creates creepy poetry after reading thousands of romance novels

05/13/2016 - 17:48

Scott Ryan

A group of researchers from Stanford, University of Massachusetts Amherst, and the Google Brain team designed an unsupervised recurrent neural network language model that generates sentences one word at a time.

Here are some of our favorite poems the system created:

he was silent for a long moment .
he was silent for a moment .
it was quiet for a moment .
it was dark and cold .
there was a pause .
it was my turn .

it made me want to cry .
no one had seen him since .
it made me feel uneasy .
no one had seen him .
the thought made me smile .
the pain was unbearable .
the crowd was silent .
the man called out .
the old man said .
the man asked .

You can find more of the poems in the research links below.

Ref: Generating Sentences from a Continuous Space. Computer Science - Learning - arXiv (25 January 2016) | arXiv:1511.06349 | PDF


The standard unsupervised recurrent neural network language model (RNNLM) generates sentences one word at a time and does not work from an explicit global distributed sentence representation. In this work, we present an RNN-based variational autoencoder language model that incorporates distributed latent representations of entire sentences. This factorization allows it to explicitly model holistic properties of sentences such as style, topic, and high-level syntactic features. Samples from the prior over these sentence representations remarkably produce diverse and well-formed sentences through simple deterministic decoding. By examining paths through this latent space, we are able to generate coherent novel sentences that interpolate between known sentences. We present techniques for solving the difficult learning problem presented by this model, demonstrate strong performance in the imputation of missing tokens, and explore many interesting properties of the latent sentence space.