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Understanding information from a collection of multiple documents, particularly those with visually rich elements, is important for document-grounded question answering.
Text generation with exemplar-based adaptive decoding
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A dataset of information-seeking questions and answers anchored in research papers
Pradeep Dasigi, Kyle Lo, Iz Beltagy, Arman Cohan, Noah A Smith, and Matt Gardner. 2021 · 2021
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Fast and accurate neural machine translation with translation memory
Qiuxiang He, Guoping Huang, Qu Cui, Li Li, and Lemao Liu. 2021 · 2021
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Docvqa: A dataset for vqa on document images
Minesh Mathew, Dimosthenis Karatzas, and CV Jawahar. 2021 · 2021
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Counterfactual vqa: A cause-effect look at language bias
Yulei Niu, Kaihua Tang, Hanwang Zhang, Zhiwu Lu, Xian-Sheng Hua, and Ji-Rong Wen. 2021 · 2021
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Wenhu Chen, Hexiang Hu, Xi Chen, Pat Verga, and William W Cohen. 2022 · 2022
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Yihao Ding, Zhe Huang, Runlin Wang, YanHang Zhang, Xianru Chen, Yuzhong Ma, Hyunsuk Chung, and Soyeon Caren Han. 2022 · 2022
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Retrieval augmented visual question answering with outside knowledge
Weizhe Lin and Bill Byrne. 2022 · 2022
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Fetaqa: Free-form table question answering
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