  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.
