  Cross-Modal sponsored search displays multi-modal advertisements (ads) when
consumers look for desired products by natural language queries in search
engines. Since multi-modal ads bring complementary details for query-ads
matching, the ability to align ads-specific information in both images and
texts is crucial for accurate and flexible sponsored search. Conventional
research mainly studies from the view of modeling the implicit correlations
between images and texts for query-ads matching, ignoring the alignment of
detailed product information and resulting in suboptimal search performance.In
this work, we propose a simple alignment network for explicitly mapping
fine-grained visual parts in ads images to the corresponding text, which
leverages the co-occurrence structure consistency between vision and language
spaces without requiring expensive labeled training data. Moreover, we propose
a novel model for cross-modal sponsored search that effectively conducts the
cross-modal alignment and query-ads matching in two separate processes. In this
way, the model matches the multi-modal input in the same language space,
resulting in a superior performance with merely half of the training data. Our
model outperforms the state-of-the-art models by 2.57% on a large commercial
dataset. Besides sponsored search, our alignment method is applicable for
general cross-modal search. We study a typical cross-modal retrieval task on
the MSCOCO dataset, which achieves consistent performance improvement and
proves the generalization ability of our method. Our code is available at
https://github.com/Pter61/AlignCMSS/
