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      R2FID: Joint Reranker기반 Fusion-In-Decoder를 이용한 오픈 도메인 테이블 질의 응답 = R2 FID: Joint Reranker in Fusion-In-Decoder for Open Domain Question Answering over Tables

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      https://www.riss.kr/link?id=A108788234

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      다국어 초록 (Multilingual Abstract)

      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.
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      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.

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      참고문헌 (Reference)

      1 J. Yang, "TableFormer: Robust transformer modeling for table-text encoding" 1 : 528-537, 2022

      2 J. Herzig, "TaPas: Weakly supervised table parsing via pre-training" 4320-4333, 2020

      3 F. Zhu, "Retrieving and reading : A comprehensive survey on open-domain question answering" CoRR 2021

      4 W. Zhong, "Reasoning over hybrid chain for tableand-text open domain question answering" 7 : 4531-4537, 2022

      5 D. Chen, "Reading Wikipedia to answer open-domain questions" 1870-1879, 2017

      6 W. Chen, "Open question answering over tables and text" CoRR 2020

      7 Y. Feng, "Multi-hop open-domain question answering over structured and unstructured knowledge" 151-156, 2022

      8 G. Izacard, "Leveraging passage retrieval with generative models for open domain question answering" 874-880, 2021

      9 C. Jun, "Korean-specific dataset for table question answering" 2022

      10 김영민 ; 임승영 ; 이현정 ; 박소윤 ; 김명지, "KorQuAD 2.0: 웹문서 기계독해를 위한 한국어 질의응답 데이터셋" 한국정보과학회 47 (47): 577-586, 2020

      1 J. Yang, "TableFormer: Robust transformer modeling for table-text encoding" 1 : 528-537, 2022

      2 J. Herzig, "TaPas: Weakly supervised table parsing via pre-training" 4320-4333, 2020

      3 F. Zhu, "Retrieving and reading : A comprehensive survey on open-domain question answering" CoRR 2021

      4 W. Zhong, "Reasoning over hybrid chain for tableand-text open domain question answering" 7 : 4531-4537, 2022

      5 D. Chen, "Reading Wikipedia to answer open-domain questions" 1870-1879, 2017

      6 W. Chen, "Open question answering over tables and text" CoRR 2020

      7 Y. Feng, "Multi-hop open-domain question answering over structured and unstructured knowledge" 151-156, 2022

      8 G. Izacard, "Leveraging passage retrieval with generative models for open domain question answering" 874-880, 2021

      9 C. Jun, "Korean-specific dataset for table question answering" 2022

      10 김영민 ; 임승영 ; 이현정 ; 박소윤 ; 김명지, "KorQuAD 2.0: 웹문서 기계독해를 위한 한국어 질의응답 데이터셋" 한국정보과학회 47 (47): 577-586, 2020

      11 P. Rajpurkar, "Know what you don’t know : Unanswerable questions for SQuAD" 784-789, 2018

      12 S. Park, "KLUE : korean language understanding evaluation" 2021

      13 D. Yu, "KG-FiD: Infusing knowledge graph in fusion-in-decoder for open-domain question answering" 4961-4974, 2022

      14 W. Chen, "HybridQA: A dataset of multihop question answering over tabular and textual data" 1026-1036, 2020

      15 S. Wang, "Evidence aggregation for answer reranking in open-domain question answering" CoRR 2017

      16 V. Karpukhin, "Dense passage retrieval for open-domain question answering" 6769-6781, 2020

      17 Y. Luo, "A survey : Complex knowledge base question answering" 46-52, 2022

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