Voice assistants such as Alexa, Siri, and Google Assistant have become
increasingly popular worldwide. However, linguistic variations, variability of
speech patterns, ambient acoustic conditions, and other such factors are often
correlated with the assistants misinterpreting the user's query. In order to
provide better customer experience, retrieval based query reformulation (QR)
systems are widely used to reformulate those misinterpreted user queries.
Current QR systems typically focus on neural retrieval model training or direct
entities retrieval for the reformulating. However, these methods rarely focus
on query expansion and entity weighting simultaneously, which may limit the
scope and accuracy of the query reformulation retrieval. In this work, we
propose a novel Query Expansion and Entity Weighting method (QEEW), which
leverages the relationships between entities in the entity catalog (consisting
of users' queries, assistant's responses, and corresponding entities), to
enhance the query reformulation performance. Experiments on Alexa annotated
data demonstrate that QEEW improves all top precision metrics, particularly 6%
improvement in top10 precision, compared with baselines not using query
expansion and weighting; and more than 5% improvement in top10 precision
compared with other baselines using query expansion and weighting.

本文提出了一种新的查询扩展和实体加权方法，利用实体目录中的实体关系改进查询重构性能，实验发现该方法相比不使用查询扩展和加权的基准模型，特别是在前 10 个结果中，精度有 6% 的提高，在使用查询扩展和加权的其他基准模型中，精度有 5% 以上的提高。