Open Domain Question Answering is a challenging problem that aims to generate an answer where reference documents relevant to a question are not provided. Considering that the importance of the QA system in structured data such as tables has recently ...
Open Domain Question Answering is a challenging problem that aims to generate an answer where reference documents relevant to a question are not provided. Considering that the importance of the QA system in structured data such as tables has recently gradually increased, this paper presents a method for table open domain question answering of Korean, focusing on tabular contents appearing in Wikipedia. In addition, we extensively apply the Joint Reranker based Fusion-In-Decoder to address limitations entailed in table retrieval, Resulting methods based on Joint Reranker led to improvements of an EM of 3.36 and a F1-Score of 3.25 over open domain question answering tasks.