  Work in information retrieval has traditionally focused on ranking and
relevance: given a query, return some number of results ordered by relevance to
the user. However, the problem of determining how many results to return, i.e.
how to optimally truncate the ranked result list, has received less attention
despite being of critical importance in a range of applications. Such
truncation is a balancing act between the overall relevance, or usefulness of
the results, with the user cost of processing more results. In this work, we
propose Choppy, an assumption-free model based on the widely successful
Transformer architecture, to the ranked list truncation problem. Needing
nothing more than the relevance scores of the results, the model uses a
powerful multi-head attention mechanism to directly optimize any user-defined
IR metric. We show Choppy improves upon recent state-of-the-art methods.

Information Retrieval;Computation and Language;Machine Learning;Machine Learning