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.

本文提出使用子词语言模型实现查询建议的快速准确生成，并介绍了一种重迹迹算法和近似边缘化的重排序方法来解决子词语言模型引入的问题。通过使用新的评估指标均可恢复长度（MRL），我们的模型可在保持生成结果质量相似的情况下实现最多2.5倍的速度提升，并进行了全面细致的分析。

查询自动完成的子词语言模型