Unit:
Κατεύθυνση Μεγάλα Δεδομένα και Τεχνητή ΝοημοσύνηΠληροφορική
Supervisors info:
Georgia Koutrika, Research Director, Athena Research Center
Nassos Katsamanis, Principal Researcher, Athena Research Center
Kurt Stockinger, Professor, Zurich University of Applied Sciences
Original Title:
Verbalising Query Results to Text
Translated title:
Verbalising Query Results to Text
Summary:
Database democratization focuses on making databases accessible to non-expert users
that are not familiar with database query languages like SQL. In this direction, a lot of
effort has already been put into two problems: Text-to-SQL, which focuses on translating a
natural language query to SQL, and SQL-to-Text, which is the inverse problem. However,
work has lagged behind in explaining query results in natural language. We first define the
Query Results-to-Text problem as: given the results of a query, produce a natural language
verbalisation describing these results. Then we attempt solving Query Results-to-Text by
defining a model, namely QR2T. We propose pretraining QR2T using real-world table
datasets focusing on table understanding. We use a preprocessing step that transforms
the query so that the query results, which are the input of our model, include additional
information, which QR2T can utilize leading to a more informative verbalisation. Finally,
we create two Query Results-to-Text benchmarks, which are the first datasets that contain
query result verbalisations for both fine-tuning and evaluation.
Main subject category:
Technology - Computer science
Keywords:
machine learning, deep learning, model pretraining
