  Semantic answer type prediction (SMART) is known to be a useful step towards
effective question answering (QA) systems. The SMART task involves predicting
the top-$k$ knowledge graph (KG) types for a given natural language question.
This is challenging due to the large number of types in KGs. In this paper, we
propose use of extreme multi-label classification using Transformer models
(XBERT) by clustering KG types using structural and semantic features based on
question text. We specifically improve the clustering stage of the XBERT
pipeline using textual and structural features derived from KGs. We show that
these features can improve end-to-end performance for the SMART task, and yield
state-of-the-art results.
