This paper studies the lower bound complexity for minimax optimization problem whose objective function is the average of $n$ individual smooth convex-concave functions.
Since the synonymous relations between queries and keywords are quite scarce, the traditional information retrieval framework is inefficient in this scenario.
This framework has been successfully applied to Baidu's sponsored search system, which yields a significant improvement in revenue.
In this paper we propose a unified approach for supporting different generation manners of machine translation, including autoregressive, semi-autoregressive, and refinement-based non-autoregressive models.
Firstly, we devise a Trie-based translation model to make a data increment.
no code implementations • 5 Aug 2020 • Yijiang Lian, Zhijie Chen, Xin Pei, Shuang Li, Yifei Wang, Yuefeng Qiu, Zhiheng Zhang, Zhipeng Tao, Liang Yuan, Hanju Guan, Kefeng Zhang, Zhigang Li, Xiaochun Liu
Industrial sponsored search system (SSS) can be logically divided into three modules: keywords matching, ad retrieving, and ranking.
no code implementations • 2 Feb 2019 • Yijiang Lian, Zhijie Chen, Jinlong Hu, Kefeng Zhang, Chunwei Yan, Muchenxuan Tong, Wenying Han, Hanju Guan, Ying Li, Ying Cao, Yang Yu, Zhigang Li, Xiaochun Liu, Yue Wang
In this paper, we present a generative retrieval method for sponsored search engine, which uses neural machine translation (NMT) to generate keywords directly from query.