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In the era of deep learning, modeling for most NLP tasks has converged to several mainstream paradigms.
A neural probabilistic language model
Yoshua Bengio, Réjean Ducharme, and Pascal Vincent. 2000 · 2000
Earlier work this paper cites.
Conditional random fields: Probabilistic models for segmenting and labeling sequence data
John D. Lafferty, Andrew McCallum, and Fernando C. N. Pereira. 2001 · 2001
Earlier work this paper cites.
The PASCAL recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005 · 2005
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Semantic parsing as machine translation
Jacob Andreas, Andreas Vlachos, and Stephen Clark. 2013 · 2013
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A fast and accurate dependency parser using neural networks
Danqi Chen and Christopher D. Manning. 2014 · 2014
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Convolutional neural networks for sentence classification
Yoon Kim. 2014 · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014 · 2014
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Relation classification via convolutional deep neural network
Daojian Zeng, Kang Liu, Siwei Lai, Guangyou Zhou, and Jun Zhao. 2014 · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
Earlier work this paper cites.
Transition-based dependency parsing with stack long short-term memory
Chris Dyer, Miguel Ballesteros, Wang Ling, Austin Matthews, and Noah A. Smith. 2015 · 2015
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Grammar as a foreign language
Oriol Vinyals, Lukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, and Geoffrey E. Hinton. 2015 · 2015
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Neural summarization by extracting sentences and words
Jianpeng Cheng and Mirella Lapata. 2016 · 2016
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Parsing as language modeling
Do Kook Choe and Eugene Charniak. 2016 · 2016
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Incorporating copying mechanism in sequence-to-sequence learning
Jiatao Gu, Zhengdong Lu, Hang Li, and Victor O. K. Li. 2016 · 2016
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Neural architectures for named entity recognition
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016 · 2016
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Recurrent neural network for text classification with multi-task learning
Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. 2016 · 2016
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Multi-task sequence to sequence learning
Minh-Thang Luong, Quoc V. Le, Ilya Sutskever, Oriol Vinyals, and Lukasz Kaiser. 2016 · 2016
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End-to-end sequence labeling via bi-directional lstm-cnns-crf
Xuezhe Ma and Eduard H. Hovy. 2016 · 2016
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Attention-based LSTM for aspect-level sentiment classification
Yequan Wang, Minlie Huang, Xiaoyan Zhu, and Li Zhao. 2016 · 2016
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Reading wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017a · 2017
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Enhanced LSTM for natural language inference
Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, and Diana Inkpen. 2017b · 2017
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Convolutional sequence to sequence learning
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin. 2017 · 2017
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Improving neural language models with a continuous cache
Edouard Grave, Armand Joulin, and Nicolas Usunier. 2017 · 2017
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Zero-shot relation extraction via reading comprehension
Omer Levy, Minjoon Seo, Eunsol Choi, and Luke Zettlemoyer. 2017 · 2017
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Bidirectional attention flow for machine comprehension
Min Joon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Dynamic coattention networks for question answering
Caiming Xiong, Victor Zhong, and Richard Socher. 2017 · 2017
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Constituent parsing as sequence labeling
Carlos Gómez-Rodríguez and David Vilares. 2018 · 2018
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Non-autoregressive neural machine translation
Jiatao Gu, James Bradbury, Caiming Xiong, Victor O. K. Li, and Richard Socher. 2018 · 2018
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Seq2seq dependency parsing
Zuchao Li, Jiaxun Cai, Shexia He, and Hai Zhao. 2018 · 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 · 2018
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Collecting diverse natural language inference problems for sentence representation evaluation
Adam Poliak, Aparajita Haldar, Rachel Rudinger, J. Edward Hu, Ellie Pavlick, Aaron Steven White, and Benjamin Van Durme. 2018 · 2018
Cited alongside, same era.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R. Bowman. 2018 · 2018
Cited alongside, same era.
SGM: sequence generation model for multi-label classification
Pengcheng Yang, Xu Sun, Wei Li, Shuming Ma, Wei Wu, and Houfeng Wang. 2018a · 2018
Cited alongside, same era.
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. 2018b · 2018
Cited alongside, same era.
Extracting relational facts by an end-to-end neural model with copy mechanism
Xiangrong Zeng, Daojian Zeng, Shizhu He, Kang Liu, and Jun Zhao. 2018 · 2018
Cited alongside, same era.
Discontinuous constituent parsing as sequence labeling
David Vilares and Carlos Gómez-Rodríguez. 2020 · 2020
Later among the works it cites.
Corefqa: Coreference resolution as query-based span prediction
Wei Wu, Fei Wang, Arianna Yuan, Fei Wu, and Jiwei Li. 2020 · 2020
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Universal natural language processing with limited annotations: Try few-shot textual entailment as a start
Wenpeng Yin, Nazneen Fatema Rajani, Dragomir R. Radev, Richard Socher, and Caiming Xiong. 2020 · 2020
Later among the works it cites.
Named entity recognition as dependency parsing
Juntao Yu, Bernd Bohnet, and Massimo Poesio. 2020 · 2020
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Asking effective and diverse questions: A machine reading comprehension based framework for joint entity-relation extraction
Tianyang Zhao, Zhao Yan, Yunbo Cao, and Zhoujun Li. 2020 · 2020
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Transformer-xl: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime G. Carbonell, Quoc Viet Le, and Ruslan Salakhutdinov. 2019 · 2019
Cited alongside, same era.
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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Merge and label: A novel neural network architecture for nested NER
Joseph Fisher and Andreas Vlachos. 2019 · 2019
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Dialog state tracking: A neural reading comprehension approach
Shuyang Gao, Abhishek Sethi, Sanchit Agarwal, Tagyoung Chung, and Dilek Hakkani-Tür. 2019 · 2019
Cited alongside, same era.
Entity-relation extraction as multi-turn question answering
Xiaoya Li, Fan Yin, Zijun Sun, Xiayu Li, Arianna Yuan, Duo Chai, Mingxin Zhou, and Jiwei Li. 2019 · 2019
Cited alongside, same era.
MASS: masked sequence to sequence pre-training for language generation
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. 2019 · 2019
Cited alongside, same era.
Viable dependency parsing as sequence labeling
Michalina Strzyz, David Vilares, and Carlos Gómez-Rodríguez. 2019 · 2019
Cited alongside, same era.
Ming Zhong, Pengfei Liu, Yiran Chen, Danqing Wang, Xipeng Qiu, and Xuanjing Huang. 2020 · 2020
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HTLM: hyper-text pre-training and prompting of language models
Armen Aghajanyan, Dmytro Okhonko, Mike Lewis, Mandar Joshi, Hu Xu, Gargi Ghosh, and Luke Zettlemoyer. 2021 · 2021
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Template-based named entity recognition using BART
Leyang Cui, Yu Wu, Jian Liu, Sen Yang, and Yue Zhang. 2021 · 2021
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Spanner: Named entity re-/recognition as span prediction
Jinlan Fu, Xuanjing Huang, and Pengfei Liu. 2021 · 2021
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Dependency parsing as mrc-based span-span prediction
Leilei Gan, Yuxian Meng, Kun Kuang, Xiaofei Sun, Chun Fan, Fei Wu, and Jiwei Li. 2021 · 2021
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Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2021 · 2021
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Read, retrospect, select: An MRC framework to short text entity linking
Yingjie Gu, Xiaoye Qu, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan, and Xiaolin Gui. 2021 · 2021
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WARP: word-level adversarial reprogramming
Karen Hambardzumyan, Hrant Khachatrian, and Jonathan May. 2021 · 2021
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PTR: prompt tuning with rules for text classification
Xu Han, Weilin Zhao, Ning Ding, Zhiyuan Liu, and Maosong Sun. 2021 · 2021
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Knowledgeable prompt-tuning: Incorporating knowledge into prompt verbalizer for text classification
Shengding Hu, Ning Ding, Huadong Wang, Zhiyuan Liu, Juanzi Li, and Maosong Sun. 2021 · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
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Sentiprompt: Sentiment knowledge enhanced prompt-tuning for aspect-based sentiment analysis
Chengxi Li, Feiyu Gao, Jiajun Bu, Lu Xu, Xiang Chen, Yu Gu, Zirui Shao, Qi Zheng, Ningyu Zhang, Yongpan Wang, and Zhi Yu. 2021 · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
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A joint training dual-mrc framework for aspect based sentiment analysis
Yue Mao, Yi Shen, Chao Yu, and Longjun Cai. 2021 · 2021
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Structured prediction as translation between augmented natural languages
Giovanni Paolini, Ben Athiwaratkun, Jason Krone, Jie Ma, Alessandro Achille, Rishita Anubhai, Cícero Nogueira dos Santos, Bing Xiang, and Stefano Soatto. 2021 · 2021
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BANG: bridging autoregressive and non-autoregressive generation with large scale pretraining
Weizhen Qi, Yeyun Gong, Jian Jiao, Yu Yan, Weizhu Chen, Dayiheng Liu, Kewen Tang, Houqiang Li, Jiusheng Chen, Ruofei Zhang, Ming Zhou, and Nan Duan. 2021 · 2021
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Learning how to ask: Querying lms with mixtures of soft prompts
Guanghui Qin and Jason Eisner. 2021 · 2021
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How many data points is a prompt worth?
Teven Le Scao and Alexander M. Rush. 2021 · 2021
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Exploiting cloze-questions for few-shot text classification and natural language inference
Timo Schick and Hinrich Schütze. 2021a · 2021
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It’s not just size that matters: Small language models are also few-shot learners
Timo Schick and Hinrich Schütze. 2021b · 2021
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Yi Sun, Yu Zheng, Chao Hao, and Hangping Qiu. 2021 · 2021
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Entailment as few-shot learner
Sinong Wang, Han Fang, Madian Khabsa, Hanzi Mao, and Hao Ma. 2021 · 2021
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Graph-free multi-hop reading comprehension: A select-to-guide strategy
Bohong Wu, Zhuosheng Zhang, and Hai Zhao. 2021 · 2021
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A unified generative framework for aspect-based sentiment analysis
Hang Yan, Junqi Dai, Tuo Ji, Xipeng Qiu, and Zheng Zhang. 2021a · 2021
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A unified generative framework for various NER subtasks
Hang Yan, Tao Gui, Junqi Dai, Qipeng Guo, Zheng Zhang, and Xipeng Qiu. 2021b · 2021
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Factual probing is [MASK]: learning vs. learning to recall
Zexuan Zhong, Dan Friedman, and Danqi Chen. 2021 · 2021
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