Data Discovery using Natural Language Questions via a Self-Supervised Approach

Data discovery systems help users identify relevant data among large table collections. Users express their discovery needs with a program or a set of keywords. Users may express complex queries using programs but it requires expertise. Keyword search is accessible to a larger audience but limits the types of queries supported. An interesting approach is learned discovery systems which find tables given natural language questions. Unfortunately, these systems require a training dataset for each table collection. And because collecting training data is expensive, this limits their adoption. In this paper, we introduce a self-supervised approach to assemble training datasets and train learned discovery systems without human intervention. It requires addressing several challenges, including the design of self-supervised strategies for data discovery, table representation strategies to feed to the models, and relevance models that work well with the synthetically generated questions. We combine all the above contributions into a system, S2LD, that solves the problem end to end. The evaluation results demonstrate the new techniques outperform state-of-the-art approaches on wellknown benchmarks. All in all, the technique is a stepping stone towards building learned discovery systems. The code is open-sourced at https://github.com/TheDataStation/open_table_discovery.