  Recently, Nogueira et al. [2019] proposed a new approach to document
expansion based on a neural Seq2Seq model, showing significant improvement on
short text retrieval task. However, this approach needs a large amount of
in-domain training data. In this paper, we show that this neural document
expansion approach can be effectively adapted to standard IR tasks, where
labels are scarce and many long documents are present.
