  A core step in statistical data-to-text generation concerns learning
correspondences between structured data representations (e.g., facts in a
database) and associated texts. In this paper we aim to bootstrap generators
from large scale datasets where the data (e.g., DBPedia facts) and related
texts (e.g., Wikipedia abstracts) are loosely aligned. We tackle this
challenging task by introducing a special-purpose content selection mechanism.
We use multi-instance learning to automatically discover correspondences
between data and text pairs and show how these can be used to enhance the
content signal while training an encoder-decoder architecture. Experimental
results demonstrate that models trained with content-specific objectives
improve upon a vanilla encoder-decoder which solely relies on soft attention.
