3 research outputs found
Solo: Data Discovery Using Natural Language Questions Via A Self-Supervised Approach
Most deployed data discovery systems, such as Google Datasets, and open data
portals only support keyword search. Keyword search is geared towards general
audiences but limits the types of queries the systems can answer. We propose a
new system that lets users write natural language questions directly. A major
barrier to using this learned data discovery system is it needs
expensive-to-collect training data, thus limiting its utility. 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, Solo, that solves the problem end to
end. The evaluation results demonstrate the new techniques outperform
state-of-the-art approaches on well-known 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/soloComment: To appear at Sigmod 202