  Related or ideal follow-up suggestions to a web query in search engines are
often optimized based on several different parameters -- relevance to the
original query, diversity, click probability etc. One or many rankers may be
trained to score each suggestion from a candidate pool based on these factors.
These scorers are usually pairwise classification tasks where each training
example consists of a user query and a single suggestion from the list of
candidates. We propose an architecture that takes all candidate suggestions
associated with a given query and outputs a suggestion block. We discuss the
benefits of such an architecture over traditional approaches and experiment
with further enforcing each individual metric through mixed-objective training.
