论文标题
表格：用自然语言查询表格数据
TableQuery: Querying tabular data with natural language
论文作者
论文摘要
本文介绍了TableQuery，这是一种使用深度学习模型来查询表格数据的新颖工具，以回答自由文本的问题。对编号数据的问题回答的现有深度学习方法具有各种限制，例如必须将整个表作为输入到神经网络模型中，这使得它们不适合大多数真实世界应用程序。由于现实世界中的数据可能包含数百万行，因此可能不完全适合内存。此外，数据可以存储在实时更新的实时数据库中，并且在每次更新时将整个数据库序列化为神经网络友好格式是不切实际的。在TableQuery中，我们使用预先培训的深度学习模型来回答自由文本的问题，以将自然语言查询转换为结构化查询，可以根据数据库或电子表格进行运行。此方法消除了将整个数据拟合到内存以及序列化数据库的需求。此外，在HuggingFace Model Hub（7）等平台上很容易获得对自由文本的回答预先培训的深度学习模型。 TableQuery不需要重新训练；当可以使用以更好的性能回答的新训练的问答模型时，它可以替换TableQuery中的现有模型。
This paper presents TableQuery, a novel tool for querying tabular data using deep learning models pre-trained to answer questions on free text. Existing deep learning methods for question answering on tabular data have various limitations, such as having to feed the entire table as input into a neural network model, making them unsuitable for most real-world applications. Since real-world data might contain millions of rows, it may not entirely fit into the memory. Moreover, data could be stored in live databases, which are updated in real-time, and it is impractical to serialize an entire database to a neural network-friendly format each time it is updated. In TableQuery, we use deep learning models pre-trained for question answering on free text to convert natural language queries to structured queries, which can be run against a database or a spreadsheet. This method eliminates the need for fitting the entire data into memory as well as serializing databases. Furthermore, deep learning models pre-trained for question answering on free text are readily available on platforms such as HuggingFace Model Hub (7). TableQuery does not require re-training; when a newly trained model for question answering with better performance is available, it can replace the existing model in TableQuery.
