RINK: Reader-Inherited Evidence Reranker for Table-and-Text Open Domain Question Answering


  • Eunhwan Park Jeonbuk National University
  • Sung-Min Lee Jeonbuk National University
  • Dearyong Seo Naver Corporation
  • Seonhoon Kim Coupang
  • Inho Kang Naver Corporation
  • Seung-Hoon Na Jeonbuk National University




SNLP: Question Answering, SNLP: Applications


Most approaches used in open-domain question answering on hybrid data that comprises both tabular-and-textual contents are based on a Retrieval-Reader pipeline in which the retrieval module finds relevant 
“heterogenous” evidence for a given question and the reader module generates an answer from the retrieved evidence. In this paper, we present a Retriever-Reranker-Reader framework by newly proposing a Reader-INherited evidence reranKer (RINK) where a reranker module is designed by finetuning the reader’s neural architecture based on a simple prompting method. Our underlying assumption of reusing the reader’s module for the reranker is that the reader’s ability to generating an answer from evidence contains the knowledge required for the reranking, because the reranker needs to “read” in-depth a question and evidences more carefully and elaborately than a baseline retriever. Furthermore, we present a simple and effective pretraining method by extensively deploying the commonly used data augmentation methods of cell corruption and cell reordering based on the pretraining tasks - tabular-and-textual entailment and cross-modal masked language modeling. Experimental results on OTT-QA, a large-scale table-and-text open-domain question answering dataset, show that the proposed RINK armed with our pretraining procedure makes improvements over the baseline reranking method and leads to state-of-the-art performance.




How to Cite

Park, E., Lee, S.-M., Seo, D., Kim, S., Kang, I., & Na, S.-H. (2023). RINK: Reader-Inherited Evidence Reranker for Table-and-Text Open Domain Question Answering. Proceedings of the AAAI Conference on Artificial Intelligence, 37(11), 13446-13456. https://doi.org/10.1609/aaai.v37i11.26577



AAAI Technical Track on Speech & Natural Language Processing