  The dominant paradigm of textual question answering systems is based on
end-to-end neural networks, which excels at answering natural language
questions but falls short on complex ones. This stands in contrast to the broad
adaptation of semantic parsing approaches over structured data sources (e.g.,
relational database, knowledge graphs), that convert natural language questions
to logical forms and execute them with query engines. Towards combining the
strengths of neural and symbolic methods, we propose a framework of question
parsing and execution on textual QA. It comprises two central pillars: (1) We
parse the question of varying complexity into an intermediate representation,
named H-expression, which is composed of simple questions as the primitives and
symbolic operations representing the relationships among them; (2) To execute
the resulting H-expressions, we design a hybrid executor, which integrates the
deterministic rules to translate the symbolic operations with a drop-in neural
reader network to answer each decomposed simple question. Hence, the proposed
framework can be viewed as a top-down question parsing followed by a bottom-up
answer backtracking. The resulting H-expressions closely guide the execution
process, offering higher precision besides better interpretability while still
preserving the advantages of the neural readers for resolving its primitive
elements. Our extensive experiments on MuSiQue, 2WikiQA, HotpotQA, and NQ show
that the proposed parsing and hybrid execution framework outperforms existing
approaches in supervised, few-shot, and zero-shot settings, while also
effectively exposing its underlying reasoning process.
