  Ads relevance models are crucial in determining the relevance between user
search queries and ad offers, often framed as a classification problem. The
complexity of modeling increases significantly with multiple ad types and
varying scenarios that exhibit both similarities and differences. In this work,
we introduce a novel multi-faceted attention model that performs task aware
feature combination and cross task interaction modeling. Our technique
formulates the feature combination problem as "language" modeling with
auto-regressive attentions across both feature and task dimensions.
Specifically, we introduce a new dimension of task ID encoding for task
representations, thereby enabling precise relevance modeling across diverse ad
scenarios with substantial improvement in generality capability for unseen
tasks. We demonstrate that our model not only effectively handles the increased
computational and maintenance demands as scenarios proliferate, but also
outperforms generalized DNN models and even task-specific models across a
spectrum of ad applications using a single unified model.
