Automated Question Generation on Tabular Data for Conversational Data Exploration

• URL: http://arxiv.org/abs/2407.12859v1
• Date: Wed, 10 Jul 2024 08:07:05 GMT
• Title: Automated Question Generation on Tabular Data for Conversational Data Exploration
• Authors: Ritwik Chaudhuri, Rajmohan C, Kirushikesh DB, Arvind Agarwal,
• Abstract summary: We propose a system that recommends interesting questions in natural language based on relevant slices of a dataset in a conversational setting. We use our own fine-tuned variation of a pre-trained language model(T5) to generate natural language questions in a specific manner.
• Score: 1.2574534342156884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Exploratory data analysis (EDA) is an essential step for analyzing a dataset to derive insights. Several EDA techniques have been explored in the literature. Many of them leverage visualizations through various plots. But it is not easy to interpret them for a non-technical user, and producing appropriate visualizations is also tough when there are a large number of columns. Few other works provide a view of some interesting slices of data but it is still difficult for the user to draw relevant insights from them. Of late, conversational data exploration is gaining a lot of traction among non-technical users. It helps the user to explore the dataset without having deep technical knowledge about the data. Towards this, we propose a system that recommends interesting questions in natural language based on relevant slices of a dataset in a conversational setting. Specifically, given a dataset, we pick a select set of interesting columns and identify interesting slices of such columns and column combinations based on few interestingness measures. We use our own fine-tuned variation of a pre-trained language model(T5) to generate natural language questions in a specific manner. We then slot-fill values in the generated questions and rank them for recommendations. We show the utility of our proposed system in a coversational setting with a collection of real datasets.

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