  Semi-structured query systems for document-oriented databases have many real
applications. One particular application that we are interested in is matching
each financial receipt image with its corresponding place of interest (POI,
e.g., restaurant) in the nationwide database. The problem is especially
challenging in the real production environment where many similar or incomplete
entries exist in the database and queries are noisy (e.g., errors in optical
character recognition). In this work, we aim to address practical challenges
when using embedding-based retrieval for the query grounding problem in
semi-structured data. Leveraging recent advancements in deep language encoding
for retrieval, we conduct extensive experiments to find the most effective
combination of modules for the embedding and retrieval of both query and
database entries without any manually engineered component. The proposed model
significantly outperforms the conventional manual pattern-based model while
requiring much less development and maintenance cost. We also discuss some core
observations in our experiments, which could be helpful for practitioners
working on a similar problem in other domains.
