  Question answering over knowledge bases (KBs) aims to answer natural language
questions with factual information such as entities and relations in KBs.
Previous methods either generate logical forms that can be executed over KBs to
obtain final answers or predict answers directly. Empirical results show that
the former often produces more accurate answers, but it suffers from
non-execution issues due to potential syntactic and semantic errors in the
generated logical forms. In this work, we propose a novel framework DecAF that
jointly generates both logical forms and direct answers, and then combines the
merits of them to get the final answers. Moreover, different from most of the
previous methods, DecAF is based on simple free-text retrieval without relying
on any entity linking tools -- this simplification eases its adaptation to
different datasets. DecAF achieves new state-of-the-art accuracy on WebQSP,
FreebaseQA, and GrailQA benchmarks, while getting competitive results on the
ComplexWebQuestions benchmark.
