  Current neural query auto-completion (QAC) systems rely on character-level
language models, but they slow down when queries are long. We present how to
utilize subword language models for the fast and accurate generation of query
completion candidates. Representing queries with subwords shorten a decoding
length significantly. To deal with issues coming from introducing subword
language model, we develop a retrace algorithm and a reranking method by
approximate marginalization. As a result, our model achieves up to 2.5 times
faster while maintaining a similar quality of generated results compared to the
character-level baseline. Also, we propose a new evaluation metric, mean
recoverable length (MRL), measuring how many upcoming characters the model
could complete correctly. It provides more explicit meaning and eliminates the
need for prefix length sampling for existing rank-based metrics. Moreover, we
performed a comprehensive analysis with ablation study to figure out the
importance of each component.
