  In a sponsored search engine, generative retrieval models are recently
proposed to mine relevant advertisement keywords for users' input queries.
Generative retrieval models generate outputs token by token on a path of the
target library prefix tree (Trie), which guarantees all of the generated
outputs are legal and covered by the target library. In actual use, we found
several typical problems caused by Trie-constrained searching length. In this
paper, we analyze these problems and propose a looking ahead strategy for
generative retrieval models named ProphetNet-Ads. ProphetNet-Ads improves the
retrieval ability by directly optimizing the Trie-constrained searching space.
We build a dataset from a real-word sponsored search engine and carry out
experiments to analyze different generative retrieval models. Compared with
Trie-based LSTM generative retrieval model proposed recently, our single model
result and integrated result improve the recall by 15.58\% and 18.8\%
respectively with beam size 5. Case studies further demonstrate how these
problems are alleviated by ProphetNet-Ads clearly.
