  Table-to-text generation aims to translate the structured data into the
unstructured text. Most existing methods adopt the encoder-decoder framework to
learn the transformation, which requires large-scale training samples. However,
the lack of large parallel data is a major practical problem for many domains.
In this work, we consider the scenario of low resource table-to-text
generation, where only limited parallel data is available. We propose a novel
model to separate the generation into two stages: key fact prediction and
surface realization. It first predicts the key facts from the tables, and then
generates the text with the key facts. The training of key fact prediction
needs much fewer annotated data, while surface realization can be trained with
pseudo parallel corpus. We evaluate our model on a biography generation
dataset. Our model can achieve $27.34$ BLEU score with only $1,000$ parallel
data, while the baseline model only obtain the performance of $9.71$ BLEU
score.
