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This paper describes N-XKT (Neural encoding based on eXplanatory Knowledge Transfer), a novel method for the automatic transfer of explanatory knowledge through neural encoding mechanisms.
Answering elementary science questions by constructing coherent scenes using background knowledge
Yang Li and Peter Clark. 2015 · 2012
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Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013 · 2013
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Knowledge graph embedding by translating on hyperplanes
Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014 · 2014
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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Learning entity and relation embeddings for knowledge graph completion
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015 · 2015
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What’s in an explanation? characterizing knowledge and inference requirements for elementary science exams
Peter Jansen, Niranjan Balasubramanian, Mihai Surdeanu, and Peter Clark. 2016 · 2016
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Science question answering using instructional materials
Mrinmaya Sachan, Kumar Dubey, and Eric Xing. 2016 · 2016
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Answering complex questions using open information extraction
Tushar Khot, Ashish Sabharwal, and Peter Clark. 2017 · 2017
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RACE: Large-scale ReAding comprehension dataset from examinations
Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy. 2017 · 2017
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Conceptnet 5.5: An open multilingual graph of general knowledge
Robyn Speer, Joshua Chin, and Catherine Havasi. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Think you have solved question answering? try arc, the ai2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018 · 2018
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WorldTree: A corpus of explanation graphs for elementary science questions supporting multi-hop inference
Peter Jansen, Elizabeth Wainwright, Steven Marmorstein, and Clayton Morrison. 2018 · 2018
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Question answering as global reasoning over semantic abstractions
Daniel Khashabi, Tushar Khot, Ashish Sabharwal, and D. Roth. 2018 · 2018
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Scitail: A textual entailment dataset from science question answering
Tushar Khot, Ashish Sabharwal, and Peter Clark. 2018 · 2018
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Can a suit of armor conduct electricity? a new dataset for open book question answering
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. 2018 · 2018
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MCScript: A novel dataset for assessing machine comprehension using script knowledge
Simon Ostermann, Ashutosh Modi, Michael Roth, Stefan Thater, and Manfred Pinkal. 2018 · 2018
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Improving language understanding by generative pre-training
A. Radford. 2018 · 2018
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Yuanfudao at SemEval-2018 task 11: Three-way attention and relational knowledge for commonsense machine comprehension
Liang Wang, Meng Sun, Wei Zhao, Kewei Shen, and Jingming Liu. 2018 · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
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Sanity check: A strong alignment and information retrieval baseline for question answering
Vikas Yadav, Rebecca Sharp, and M. Surdeanu. 2018 · 2018
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Learning to attend on essential terms: An enhanced retriever-reader model for open-domain question answering
Jianmo Ni, Chenguang Zhu, Weizhu Chen, and Julian McAuley. 2019 · 2019
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Improving retrieval-based question answering with deep inference models
George-Sebastian Pîrtoacă, Traian Rebedea, and Ștefan Rușeți. 2019 · 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 · 2019
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Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 2019
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Improving machine reading comprehension with general reading strategies
Kai Sun, Dian Yu, Dong Yu, and Claire Cardie. 2019 · 2019
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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 · 2018
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Kg2: Learning to reason science exam questions with contextual knowledge graph embeddings
Y. Zhang, H. Dai, Kamil Toraman, and L. Song. 2018 · 2018
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Careful selection of knowledge to solve open book question answering
Pratyay Banerjee, Kuntal Kumar Pal, Arindam Mitra, and Chitta Baral. 2019 · 2019
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SciBERT: A pretrained language model for scientific text
Iz Beltagy, Kyle Lo, and Arman Cohan. 2019 · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Cognitive graph for multi-hop reading comprehension at scale
Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang, and Jie Tang. 2019 · 2019
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DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2019 · 2019
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Identifying supporting facts for multi-hop question answering with document graph networks
Mokanarangan Thayaparan, Marco Valentino, Viktor Schlegel, and André Freitas. 2019 · 2019
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Quick and (not so) dirty: Unsupervised selection of justification sentences for multi-hop question answering
Vikas Yadav, Steven Bethard, and Mihai Surdeanu. 2019 · 2019
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Improving question answering by commonsense-based pre-training
Wanjun Zhong, Duyu Tang, Nan Duan, M. Zhou, Jiahai Wang, and J. Yin. 2019 · 2019
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Learning to retrieve reasoning paths over wikipedia graph for question answering
Akari Asai, Kazuma Hashimoto, Hannaneh Hajishirzi, Richard Socher, and Caiming Xiong. 2020 · 2020
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Self-supervised knowledge triplet learning for zero-shot question answering
Pratyay Banerjee and Chitta Baral. 2020 · 2020
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From ‘f’ to ‘a’ on the n.y. regents science exams: An overview of the aristo project
Peter Clark, Oren Etzioni, Tushar Khot, Daniel Khashabi, Bhavana Mishra, Kyle Richardson, Ashish Sabharwal, Carissa Schoenick, Carissa Schoenick, Oyvind Tafjord, Niket Tandon, Sumithra Bhakthavatsalam, Dirk Groeneveld, Michal Guerquin, and Michael Schmitz. 2020 · 2020
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Qasc: A dataset for question answering via sentence composition
Tushar Khot, Peter Clark, Michal Guerquin, Peter Jansen, and Ashish Sabharwal. 2020 · 2020
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A survey on explainability in machine reading comprehension
Mokanarangan Thayaparan, Marco Valentino, and André Freitas. 2020 · 2020
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WorldTree v2: A corpus of science-domain structured explanations and inference patterns supporting multi-hop inference
Zhengnan Xie, Sebastian Thiem, Jaycie Martin, Elizabeth Wainwright, Steven Marmorstein, and Peter Jansen. 2020 · 2020
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Unification-based reconstruction of multi-hop explanations for science questions
Marco Valentino, Mokanarangan Thayaparan, and André Freitas. 2021 · 2021
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