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Effective multi-hop question answering (QA) requires reasoning over multiple scattered paragraphs and providing explanations for answers.
Addressing a question answering challenge by combining statistical methods with inductive rule learning and reasoning
Arindam Mitra and Chitta Baral · 2016
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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Question answering by reasoning across documents with graph convolutional networks
Nicola De Cao, Wilker Aziz, and Ivan Titov · 2018
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Identifying well-formed natural language questions
Manaal Faruqui and Dipanjan Das · 2018
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Abstract meaning representation for multi-document summarization
Kexin Liao, Logan Lebanoff, and Fei Liu · 2018
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning · 2018
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Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, and Christopher D. Manning · 2018
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Hierarchical graph network for multi-hop question answering
Yuwei Fang, Siqi Sun, Zhe Gan, Rohit Pillai, Shuohang Wang, and Jingjing Liu · 2019
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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer · 2019
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Multi-hop reading comprehension through question decomposition and rescoring
Sewon Min, Victor Zhong, Luke Zettlemoyer, and Hannaneh Hajishirzi · 2019
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Dynamically fused graph network for multi-hop reasoning
Lin Qiu, Yunxuan Xiao, Yanru Qu, Hao Zhou, Lei Li, Weinan Zhang, and Yong Yu · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2019
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Amr parsing via graph-sequence iterative inference
Deng Cai and Wai Lam · 2020
Select, answer and explain: Interpretable multi-hop reading comprehension over multiple documents
Ming Tu, Kevin Huang, Guangtao Wang, Jing Huang, Xiaodong He, and Bowen Zhou · 2020
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One spring to rule them both: Symmetric amr semantic parsing and generation without a complex pipeline
Michele Bevilacqua, Rexhina Blloshmi, and Roberto Navigli · 2021
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Ensembling graph predictions for amr parsing
Thanh Lam Hoang, Gabriele Picco, Yufang Hou, Young-Suk Lee, Lam Nguyen, Dzung Phan, Vanessa López, and Ramon Fernandez Astudillo · 2021
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Leveraging abstract meaning representation for knowledge base question answering
Pavan Kapanipathi, Ibrahim Abdelaziz, Srinivas Ravishankar, Salim Roukos, Alexander Gray, Ramón Fernandez Astudillo, Maria Chang, Cristina Cornelio, Saswati Dana, Achille Fokoue-Nkoutche, et al · 2021
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Asq: Automatically generating question-answer pairs using amrs
Geetanjali Rakshit and Jeffrey Flanigan · 2021
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Hybridqa: A dataset of multi-hop question answering over tabular and textual data
Wenhu Chen, Hanwen Zha, Zhiyu Chen, Wenhan Xiong, Hong Wang, and William Wang · 2020
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Unsupervised multi-hop question answering by question generation
Liangming Pan, Wenhu Chen, Wenhan Xiong, Min-Yen Kan, and William Yang Wang · 2020
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Unsupervised question decomposition for question answering
Ethan Perez, Patrick S. H. Lewis, Wen-tau Yih, Kyunghyun Cho, and Douwe Kiela · 2020
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Investigating pretrained language models for graph-to-text generation
Leonardo FR Ribeiro, Martin Schmitt, Hinrich Schütze, and Iryna Gurevych · 2020
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Structural adapters in pretrained language models for amr-to-text generation
Leonardo FR Ribeiro, Yue Zhang, and Iryna Gurevych · 2021
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Weiwen Xu, Huihui Zhang, Deng Cai, and Wai Lam · 2021
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Adaptive information seeking for open-domain question answering
Yunchang Zhu, Liang Pang, Yanyan Lan, Huawei Shen, and Xueqi Cheng · 2021
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