  Text generation from semantic graphs is traditionally performed with
deterministic methods, which generate a unique description given an input
graph. However, the generation problem admits a range of acceptable textual
outputs, exhibiting lexical, syntactic and semantic variation. To address this
disconnect, we present two main contributions. First, we propose a stochastic
graph-to-text model, incorporating a latent variable in an encoder-decoder
model, and its use in an ensemble. Second, to assess the diversity of the
generated sentences, we propose a new automatic evaluation metric which jointly
evaluates output diversity and quality in a multi-reference setting. We
evaluate the models on WebNLG datasets in English and Russian, and show an
ensemble of stochastic models produces diverse sets of generated sentences,
while retaining similar quality to state-of-the-art models.
