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Understanding natural language questions entails the ability to break down a question into the requisite steps for computing its answer.
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Vqa: Visual question answering
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Scalable semantic parsing with partial ontologies
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Compositional semantic parsing on semi-structured tables
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Learning to compose neural networks for question answering
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Luheng He, Julian Michael, Mike Lewis, and Luke Zettlemoyer. 2016 · 2016
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Neural compositional denotational semantics for question answering
Nitish Gupta and Mike Lewis. 2018 · 2018
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The web as knowledge-base for answering complex questions
Alon Talmor and Jonathan Berant. 2018 · 2018
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Hotpotqa: A dataset for diverse, explainable multi-hop question answering
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Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task
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Transforming dependency structures to logical forms for semantic parsing
Siva Reddy, Oscar Täckström, Michael Collins, Tom Kwiatkowski, Dipanjan Das, Mark Steedman, and Mirella Lapata. 2016 · 2016
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Optimizing statistical machine translation for text simplification
Wei Xu, Courtney Napoles, Ellie Pavlick, Quanze Chen, and Chris Callison-Burch. 2016 · 2016
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The value of semantic parse labeling for knowledge base question answering
Wen-tau Yih, Matthew Richardson, Christopher Meek, Ming-Wei Chang, and Jina Suh. 2016 · 2016
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Reading Wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017 · 2017
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Allennlp: A deep semantic natural language processing platform
Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew Peters, Michael Schmitz, and Luke S. Zettlemoyer. 2017 · 2017
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Learning to reason: End-to-end module networks for visual question answering
Ronghang Hu, Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Kate Saenko. 2017 · 2017
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Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, and Dragomir R. Radev. 2018 · 2018
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Comqa: A community-sourced dataset for complex factoid question answering with paraphrase clusters
Abdalghani Abujabal, Rishiraj Saha Roy, Mohamed Yahya, and Gerhard Weikum. 2019 · 2019
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Bridging the semantic gap with sql query logs in natural language interfaces to databases
Christopher Baik, Hosagrahar Visvesvaraya Jagadish, and Yunyao Li. 2019 · 2019
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Understanding dataset design choices for multi-hop reasoning
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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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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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Towards complex text-to-sql in cross-domain database with intermediate representation
Jiaqi Guo, Zecheng Zhan, Yan Gao, Yan Xiao, Jian-Guang Lou, Ting Liu, and Dongmei Zhang. 2019 · 2019
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Gqa: A new dataset for real-world visual reasoning and compositional question answering
Drew A. Hudson and Christopher D. Manning. 2019 · 2019
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Avoiding reasoning shortcuts: Adversarial evaluation, training, and model development for multi-hop QA
Yichen Jiang and Mohit Bansal. 2019 · 2019
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Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. 2019 · 2019
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Answering complex open-domain questions through iterative query generation
Peng Qi, Xiaowen Lin, Leo Mehr, Zijian Wang, and Christopher D. Manning. 2019 · 2019
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A corpus for reasoning about natural language grounded in photographs
Alane Suhr, Stephanie Zhou, Iris Zhang, Huajun Bai, and Yoav Artzi. 2019 · 2019
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