Sponsored search auction is a crucial component of modern search engines. It
requires a set of candidate bidwords that advertisers can place bids on.
Existing methods generate bidwords from search queries or advertisement
content. However, they suffer from the data noise in <query, bidword> and
<advertisement, bidword> pairs. In this paper, we propose a triangular bidword
generation model (TRIDENT), which takes the high-quality data of paired <query,
advertisement> as a supervision signal to indirectly guide the bidword
generation process. Our proposed model is simple yet effective: by using
bidword as the bridge between search query and advertisement, the generation of
search query, advertisement and bidword can be jointly learned in the
triangular training framework. This alleviates the problem that the training
data of bidword may be noisy. Experimental results, including automatic and
human evaluations, show that our proposed TRIDENT can generate relevant and
diverse bidwords for both search queries and advertisements. Our evaluation on
online real data validates the effectiveness of the TRIDENT's generated
bidwords for product search.

本文提出了一种三角形竞价词生成模型 (TRIDENT)，通过使用竞价词作为搜索查询和广告之间的桥梁，可以联合学习搜索查询，广告和竞价词的生成过程，解决了当前方法在 <查询，竞价词> 和 <广告，竞价词> 数据噪声方面的问题，并实现了在线实际数据的验证，结果表明我们的 TRIDENT 能够为产品搜索生成相关且多样化的竞价词。