  In sponsored search, retrieving synonymous keywords is of great importance
for accurately targeted advertising. The semantic gap between queries and
keywords and the extremely high precision requirements (>= 95\%) are two major
challenges to this task. To the best of our knowledge, the problem has not been
openly discussed. In an industrial sponsored search system, the retrieved
keywords for frequent queries are usually done ahead of time and stored in a
lookup table. Considering these results as a seed dataset, we propose a
data-augmentation-like framework to improve the synonymous retrieval
performance for these frequent queries. This framework comprises two steps:
translation-based retrieval and discriminant-based filtering. Firstly, we
devise a Trie-based translation model to make a data increment. In this phase,
a Bag-of-Core-Words trick is conducted, which increased the data increment's
volume 4.2 times while keeping the original precision. Then we use a BERT-based
discriminant model to filter out nonsynonymous pairs, which exceeds the
traditional feature-driven GBDT model with 11\% absolute AUC improvement. This
method has been successfully applied to Baidu's sponsored search system, which
has yielded a significant improvement in revenue. In addition, a commercial
Chinese dataset containing 500K synonymous pairs with a precision of 95\% is
released to the public for paraphrase study
(http://ai.baidu.com/broad/subordinate?dataset=paraphrasing).
