论文标题
具有深度检索的面向文档的数据库的半结构性查询接地及其在收据和POI匹配的应用
Semi-Structured Query Grounding for Document-Oriented Databases with Deep Retrieval and Its Application to Receipt and POI Matching
论文作者
论文摘要
针对文档的数据库的半结构化查询系统具有许多实际应用程序。我们感兴趣的一个特定应用程序是将每个财务收据图像与其相应的感兴趣地点（例如POI，例如餐厅）匹配。在数据库中存在许多类似或不完整的条目的真实生产环境中，问题尤其具有挑战性，并且查询是嘈杂的（例如，光学特征识别中的错误）。在这项工作中，我们旨在解决半结构化数据中的查询接地问题的基于嵌入的检索时解决实践挑战。利用最新语言编码的最新进步进行检索，我们进行了广泛的实验，以找到最有效的模块组合，用于嵌入和检索查询和数据库条目，而没有任何手动工程的组件。提出的模型大大优于常规手动模式模型，同时需要更少的开发和维护成本。我们还讨论了实验中的一些核心观察，这可能对从事其他领域的类似问题的从业者有帮助。
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.
