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Question Answering (QA) is a widely-used framework for developing and evaluating an intelligent machine.
Semantic parsing on freebase from question-answer pairs
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang · 2013
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Antoine Bordes, Sumit Chopra, and Jason Weston · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
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Vqa: Visual question answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, and Devi Parikh · 2015
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
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Effective approaches to attention-based neural machine translation
Minh-Thang Luong, Hieu Pham, and Christopher D Manning · 2015
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Neural programmer: Inducing latent programs with gradient descent
Arvind Neelakantan, Quoc V Le, and Ilya Sutskever · 2015
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Semantic parsing via staged query graph generation: Question answering with knowledge base
Scott Wen-tau Yih, Ming-Wei Chang, Xiaodong He, and Jianfeng Gao · 2015
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Language to logical form with neural attention
Li Dong and Mirella Lapata · 2016
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Character-level question answering with attention
Xiaodong He and David Golub · 2016
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Mimic-iii, a freely accessible critical care database
Alistair EW Johnson, Tom J Pollard, Lu Shen, H Lehman Li-wei, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark · 2016
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Ms marco: a human-generated machine reading comprehension dataset
Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng · 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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Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C. Lawrence Zitnick, and Ross Girshick · 2017
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Neural symbolic machines: Learning semantic parsers on freebase with weak supervision
Chen Liang, Jonathan Berant, Quoc Le, Kenneth D Forbus, and Ni Lao · 2017
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Improving text-to-sql evaluation methodology
Catherine Finegan-Dollak, Jonathan K. Kummerfeld, Li Zhang, Karthik Ramanathan, Sesh Sadasivam, Rui Zhang, and Dragomir Radev · 2018
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Dialog-to-action: Conversational question answering over a large-scale knowledge base
Daya Guo, Duyu Tang, Nan Duan, Ming Zhou, and Jian Yin · 2018
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The natural language decathlon: Multitask learning as question answering
Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher · 2018
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emrqa: A large corpus for question answering on electronic medical records
Anusri Pampari, Preethi Raghavan, Jennifer Liang, and Jian Peng · 2018
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Structvae: Tree-structured latent variable models for semi-supervised semantic parsing
Pengcheng Yin, Chunting Zhou, Junxian He, and Graham Neubig · 2018
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Get to the point: Summarization with pointer-generator networks
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Bidirectional attention flow for machine comprehension
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Sqlnet: Generating structured queries from natural language without reinforcement learning
Xiaojun Xu, Chang Liu, and Dawn Song · 2017
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Seq2sql: Generating structured queries from natural language using reinforcement learning
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Weakly-supervised neural semantic parsing with a generative ranker
Jianpeng Cheng and Mirella Lapata · 2018
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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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Introduction to neural network based approaches for question answering over knowledge graphs
Nilesh Chakraborty, Denis Lukovnikov, Gaurav Maheshwari, Priyansh Trivedi, Jens Lehmann, and Asja Fischer · 2019
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Complex program induction for querying knowledge bases in the absence of gold programs
Amrita Saha, Ghulam Ahmed Ansari, Abhishek Laddha, Karthik Sankaranarayanan, and Soumen Chakrabarti · 2019
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Text-to-sql generation for question answering on electronic medical records
Ping Wang, Tian Shi, and Chandan K Reddy · 2020
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