论文标题
Muger $^2 $：多性证据检索和混合问题的推理回答
MuGER$^2$: Multi-Granularity Evidence Retrieval and Reasoning for Hybrid Question Answering
论文作者
论文摘要
混合问题回答（HQA）旨在回答有关异质数据的问题，包括与表单元相关的表和段落。异质数据可以向HQA模型，E.T。，列，行，细胞和链接提供不同的粒度证据。常规的HQA模型通常会检索粗或细粒的证据以推理答案。通过比较，我们发现粗粒的证据更容易检索，但对推理者的贡献较少，而细粒度的证据则相反。为了维护优势并消除了不同粒度证据的劣势，我们建议Muger $^2 $，这是一种多性证据检索和推理方法。在证据检索中，统一的猎犬旨在从异质数据中学习多个跨性证据。在答案推理中，提出了一个证据选择者，以根据所学到的多粒性证据来浏览答案读取器的细粒度证据。 HybrIDQA数据集的实验结果表明，Muger $^2 $显着提高了HQA性能。进一步的消融分析验证了检索和推理设计的有效性。
Hybrid question answering (HQA) aims to answer questions over heterogeneous data, including tables and passages linked to table cells. The heterogeneous data can provide different granularity evidence to HQA models, e.t., column, row, cell, and link. Conventional HQA models usually retrieve coarse- or fine-grained evidence to reason the answer. Through comparison, we find that coarse-grained evidence is easier to retrieve but contributes less to the reasoner, while fine-grained evidence is the opposite. To preserve the advantage and eliminate the disadvantage of different granularity evidence, we propose MuGER$^2$, a Multi-Granularity Evidence Retrieval and Reasoning approach. In evidence retrieval, a unified retriever is designed to learn the multi-granularity evidence from the heterogeneous data. In answer reasoning, an evidence selector is proposed to navigate the fine-grained evidence for the answer reader based on the learned multi-granularity evidence. Experiment results on the HybridQA dataset show that MuGER$^2$ significantly boosts the HQA performance. Further ablation analysis verifies the effectiveness of both the retrieval and reasoning designs.
