  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.
