  This paper presents a neural document expansion approach (NeuDEF) that
enriches document representations for neural ranking models. NeuDEF harvests
expansion terms from queries which lead to clicks on the document and weights
these expansion terms with learned attention. It is plugged into a standard
neural ranker and learned end-to-end. Experiments on a commercial search log
demonstrate that NeuDEF significantly improves the accuracy of state-of-the-art
neural rankers and expansion methods on queries with different frequencies.
Further studies show the contribution of click queries and learned expansion
weights, as well as the influence of document popularity of NeuDEF's
effectiveness.
