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

HPE:Answering Complex Questions over Text by Hybrid Question Parsing and Execution