Multi-Hop Open-Domain Question Answering over Structured and Unstructured Knowledge (2022)

Abstract: Open-domain question answering systems need to answer question of our interests with structured and unstructured information. However, existing approaches only select one source to generate answer or only conduct reasoning on structured information. In this paper, we pro- pose a Document-Entity Heterogeneous Graph Network, referred to as DEHG, to effectively integrate different sources of information, and conduct reasoning on heterogeneous information. DEHG employs a graph constructor to integrate structured and unstructured information, a context encoder to represent nodes and question, a heterogeneous information reasoning layer to conduct multi-hop reasoning on both information sources, and an answer decoder to generate answers for the question. Experimental results on HybirdQA dataset show that DEHG outperforms the state-of-the-art methods.

## introduction open-domain question answering (odqa) is a task to answer any form of question in general domains with provided evidence @xcite @xcite . the evidence that is used can be categorized into unstructured text like wikipedia passages @xcite @xcite and structured data like wikidata/wikitables @xcite @xcite . in practice, an ideal odqa model should be able to analyze evidence from both unstructured text and structured data sources, as both types of evidence have their own advantages: 1) the unstructured text covers more general domains; 2) the structured data has better explainability to solve complex multi-hop reasoning. one line of research accesses unstructured text and structured data independently @xcite @xcite . the input question is sent to unstructured text system (textqa) and structured knowledge base system (kbqa), and one of them is selected to output the final answer. these methods cannot combine the two sources of information properly. recently, a new line of research aggregates heterogeneous information to find the answer @xcite , which can construct connection between passages and table data. however, the method only conducts multi-hop reasoning on table data. it is difficult to handle questions that need to be answered when multi-hop reasoning on both sources is required. in this work, we propose a novel document-entity heterogeneous graph network (referred to as dehg) for open-domain question answering which can conduct multi-hop reasoning on aggregated heterogeneous information. dehg comprises a graph constructor to integrate heterogeneous information sources, a context encoder to generate representations for nodes and question, a heterogeneous information reasoning layer to explore multi-hoop connectivity of both information sources, and an answer decoder to generate answers for the question. our contributions can be summarized as follows: to the best of our knowledge, our work is the first to conduct multi-hop reasoning on integrated heterogeneous information in open-domain question answering. (2) we propose a document-entity heterogeneous graph network to analyze complex relation of heterogeneous information in open-domain question answering. (3) we present experimental results that show dehg outperforms previous state-of-the-art on hybirdqa dataset. we also perform an ablation study of our model to provide further insights. ht wt wt-1 ht-1 answer decoder context encoder question's embedding attention attention … cell phrase subgraph integration cell-phrase subgraph 2 our approach ## related work most work on qa uses structured and structured data independently @xcite @xcite @xcite @xcite @xcite @xcite . they use unstructured text system (textqa) and structured knowledge base system (kbqa) to utilize different information. these methods cannot integrate different sources of information. a new method is proposed to aggregate heterogeneous information to find answer @xcite . however, it only conducts multi-hop reasoning on table data. it is difficult to handle questions when multi-hop reasoning on both sources is required. ## conclusion we have proposed a new approach to multi-hop question answering over tabular and textual data. the approach, referred to as dehg, takes question answering as a problem of reasoning answers on the basis of a heterogeneous information graph. dehg employs bert in encoding of questions and passages respectively and generates pointers in decoding of answer generation. experimental results show that dehg significantly outperforms the state-of-the-art methods.