Abstract
Knowledge base question answering (KBQA) is an
important task in natural language processing. Existing methods for KBQA usually start with entity linking, which considers mostly named entities
found in a question as the starting points in the KB
to search for answers to the question. However,
relying only on entity linking to look for answer
candidates may not be sufficient. In this paper, we
propose to perform topic unit linking where topic
units cover a wider range of units of a KB. We use
a generation-and-scoring approach to gradually re-
fine the set of topic units. Furthermore, we use reinforcement learning to jointly learn the parameters
for topic unit linking and answer candidate ranking in an end-to-end manner. Experiments on three
commonly used benchmark datasets show that our
method consistently works well and outperforms
the previous state of the art on two datasets