Answer Candidate Type Selection: Text-to-Text Language Model for Closed
  Book Question Answering Meets Knowledge Graphs
Mikhail Salnikov Maria Lysyuk Pavel Braslavski Anton Razzhigaev Valentin Malykh Alexander Panchenko
Pre-trained Text-to-Text Language Models (LMs), such as T5 or BART yield
promising results in the Knowledge Graph Question Answering (KGQA) task.
However, the capacity of the models is limited and the quality decreases for
questions with less popular entities. In this paper, we present a novel
approach which works on top of the pre-trained Text-to-Text QA system to
address this issue. Our simple yet effective method performs filtering and
re-ranking of generated candidates based on their types derived from Wikidata
"instance_of" property.