HPE：通过混合问题解析与执行回答基于文本的复杂问题
HPE:Answering Complex Questions over Text by Hybrid Question Parsing and Execution
AI总结：
本文提出HPE框架，通过将复杂问题解析为H-expression中间表示并结合混合执行器，在文本问答中实现了神经与符号方法的优势互补，在多种设置下优于现有方法且提升了可解释性。
摘要：
文本问答系统的主流范式基于端到端神经网络，该范式擅长回答自然语言问题，但在复杂问题上表现不佳。这与语义解析方法在结构化数据源（如关系数据库、知识图谱）上的广泛适应形成对比，后者将自然语言问题转换为逻辑形式并由查询引擎执行。为了结合神经与符号方法的优势，我们提出了一种文本问答的问题解析与执行框架。它包含两个核心支柱：（1）我们将不同复杂度的问题解析为名为 H-expression 的中间表示，它由作为基元的简单问题和表示它们之间关系的符号操作组成；（2）为了执行生成的 H-expression，我们设计了一个混合执行器，它集成了用于翻译符号操作的确定性规则和一个即插即用的神经阅读器网络来回答每个分解后的简单问题。因此，所提出的框架可以被视为自顶向下的问题解析，随后是自底向上的答案回溯。生成的 H-expression 密切指导执行过程，除了提供更好的可解释性外，还提供了更高的精度，同时保留了神经阅读器在解析其基元元素方面的优势。我们在 MuSiQue、2WikiQA、HotpotQA 和 NQ 上的大量实验表明，所提出的解析与混合执行框架在监督、少样本和零样本设置下均优于现有方法，同时也有效地暴露了其底层推理过程。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
