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Who did what to whom is a major focus in natural language understanding, which is right the aim of semantic role labeling (SRL) task.
Automatic labeling of semantic roles
Daniel Gildea and Daniel Jurafsky · 2002
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The proposition bank: An annotated corpus of semantic roles
Martha Palmer, Daniel Gildea, and Paul Kingsbury · 2005
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Towards robust linguistic analysis using OntoNotes
Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Hwee Tou Ng, Anders Björkelund, Olga Uryupina, Yuchen Zhang, and Zhi Zhong · 2013
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One billion word benchmark for measuring progress in statistical language modeling
Ciprian Chelba, Tomas Mikolov, Mike Schuster, Ge Qi, Thorsten Brants, Phillipp Koehn, and Tony Robinson · 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
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Neural network language model for chinese pinyin input method engine
Shenyuan Chen, Hai Zhao, and Rui Wang · 2015
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Question-answer driven semantic role labeling: Using natural language to annotate natural language
Luheng He, Mike Lewis, and Luke Zettlemoyer · 2015
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom · 2015
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The goldilocks principle: Reading children’s books with explicit memory representations
Felix Hill, Antoine Bordes, Sumit Chopra, and Jason Weston · 2015
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End-to-end learning of semantic role labeling using recurrent neural networks
Jie Zhou and Wei Xu · 2015
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Text understanding with the attention sum reader network
Rudolf Kadlec, Martin Schmid, Ondrej Bajgar, and Jan Kleindienst · 2016
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Discourse relation sense classification using cross-argument semantic similarity based on word embeddings
Todor Mihaylov and Anette Frank · 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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Knowledge-based semantic embedding for machine translation
Chen Shi, Shujie Liu, Shuo Ren, Shi Feng, Mu Li, Ming Zhou, Xu Sun, and Houfeng Wang · 2016
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The value of semantic parse labeling for knowledge base question answering
Wen Tau Yih, Matthew Richardson, Chris Meek, Ming Wei Chang, and Jina Suh · 2016
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Fast and accurate neural word segmentation for Chinese
Deng Cai, Hai Zhao, Zhisong Zhang, Yuan Xin, Yongjian Wu, and Feiyue Huang · 2017
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Enhanced lstm for natural language inference
Qian Chen, Xiaodan Zhu, Zhenhua Ling, Si Wei, Hui Jiang, and Diana Inkpen · 2017
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Gated-attention readers for text comprehension
Bhuwan Dhingra, Hanxiao Liu, Zhilin Yang, William W. Cohen, and Ruslan Salakhutdinov · 2017
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Deep semantic role labeling: What works and what’s next
Luheng He, Kenton Lee, Mike Lewis, and Zettlemoyer · 2017
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Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang · 2017
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Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer · 2017
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Learned in translation: Contextualized word vectors
Bryan Mccann, James Bradbury, Caiming Xiong, and Richard Socher · 2017
The natural language decathlon: Multitask learning as question answering
Bryan Mccann, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher · 2018
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Did the model understand the question?
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Discourse marker augmented network with reinforcement learning for natural language inference
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Deep contextualized word representations
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Gated self-matching networks for reading comprehension and question answering
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Deep enhanced representation for implicit discourse relation recognition
Hongxiao Bai and Hai Zhao · 2018
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Simple and effective multi-paragraph reading comprehension
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BERT: Pre-training of deep bidirectional transformers for language understanding
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Large-scale QA-SRL parsing
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Multi-granularity hierarchical attention fusion networks for reading comprehension and question answering
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Exploring recombination for efficient decoding of neural machine translation
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One-shot learning for question-answering in gaokao history challenge
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Subword-augmented embedding for cloze reading comprehension
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Effective character-augmented word embedding for machine reading comprehension
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Dependency or span, end-to-end uniform semantic role labeling
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Lattice-based transformer encoder for neural machine translation
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