ProphetNet-Ads:A Looking Ahead Strategy for Generative Retrieval Models in Sponsored Search Engine

摘要：

　　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 guaran-tees 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 exper-iments to analyze different generative retrieval models.Compared with Trie-based LSTM generative retrieval model proposed recently,our sin-gle 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.

关键词： Sponsored search engine Generative retrieval model Keywords extension Information retrieval Natural language generation

作者: Weizhen Qi Yeyun Gong Yu Yan Jian Jiao Bo Shao Ruofei Zhang Houqiang Li Nan Duan Ming Zhou

作者单位: University of Science and Technology of China,Hefei,China Microsoft Research Asia,Beijing,China Microsoft,Redmond,USA Sun Yat-sen University,Guangzhou,China

会议类型: 国际会议

会议名称: 9th CCF International Conference on Natural Language Processing and Chinese Computing (NLPCC 2020)

会议地点: 郑州

会议语种:英文

页码: 1156-1168

在线出版日期: 2020-10-14（万方平台首次上网日期，不代表论文的发表时间）