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Recently generating natural language explanations has shown very promising results in not only offering interpretable explanations but also providing additional information and supervision for prediction.
Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher · 1906
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SemEval-2014 task 4: Aspect based sentiment analysis
Maria Pontiki, Dimitris Galanis, John Pavlopoulos, Harris Papageorgiou, Ion Androutsopoulos, and Suresh Manandhar · 2004
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Semi-supervised self-training of object detection models
Chuck Rosenberg, Martial Hebert, and Henry Schneiderman · 2005
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Variational em algorithms for non-gaussian latent variable models
Jason Palmer, Kenneth Kreutz-Delgado, Bhaskar D Rao, and David P Wipf · 2006
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Bert loses patience: Fast and robust inference with early exit
Wangchunshu Zhou, Canwen Xu, Tao Ge, Julian McAuley, Ke Xu, and Furu Wei · 2006
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Semeval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals
Iris Hendrickx, Su Nam Kim, Zornitsa Kozareva, Preslav Nakov, Diarmuid O Séaghdha, Sebastian Padó, Marco Pennacchiotti, Lorenza Romano, and Stan Szpakowicz · 2010
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee · 2013
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Learning from natural instructions
Dan Goldwasser and Dan Roth · 2014
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Joint concept learning and semantic parsing from natural language explanations
Shashank Srivastava, Igor Labutov, and Tom Mitchell · 2017
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Joint concept learning and semantic parsing from natural language explanations
Shashank Srivastava, Igor Labutov, and Tom Mitchell · 2017
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Teaching machines to describe images with natural language feedback
Sanja Fidler et al · 2017
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Position-aware attention and supervised data improve slot filling
Yuhao Zhang, Victor Zhong, Danqi Chen, Gabor Angeli, and Christopher D. Manning · 2017
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Position-aware attention and supervised data improve slot filling
Yuhao Zhang, Victor Zhong, Danqi Chen, Gabor Angeli, and Christopher D Manning · 2017
Cited alongside, same era.
Men also like shopping: Reducing gender bias amplification using corpus-level constraints
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang · 2017
Cited alongside, same era.
e-snli: Natural language inference with natural language explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom · 2018
Cited alongside, same era.
Training classifiers with natural language explanations
Braden Hancock, Martin Bringmann, Paroma Varma, Percy Liang, Stephanie Wang, and Christopher Ré · 2018
Cited alongside, same era.
Inducing relational knowledge from bert
Zied Bouraoui, Jose Camacho-Collados, and Steven Schockaert · 2019
Cited alongside, same era.
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le · 2019
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut · 2019
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 2019
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An analysis of gender bias studies in natural language processing
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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H Miller, and Sebastian Riedel · 2019
Cited alongside, same era.
Unified language model pre-training for natural language understanding and generation
Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon · 2019
Cited alongside, same era.
Sentence-BERT: Sentence embeddings using Siamese BERT-networks
Nils Reimers and Iryna Gurevych · 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
Cited alongside, same era.
Matching the blanks: Distributional similarity for relation learning
Livio Baldini Soares, Nicholas FitzGerald, Jeffrey Ling, and Tom Kwiatkowski · 2019
Cited alongside, same era.
Aspect-based sentiment classification with aspect-specific graph convolutional networks
Chen Zhang, Qiuchi Li, and Dawei Song · 2019
Cited alongside, same era.
Bert post-training for review reading comprehension and aspect-based sentiment analysis
Hu Xu, Bing Liu, Lei Shu, and Philip S Yu · 2019
Cited alongside, same era.
Marta R Costa-jussà · 2019
Later among the works it cites.
Nero: A neural rule grounding framework for label-efficient relation extraction
Wenxuan Zhou, Hongtao Lin, Bill Yuchen Lin, Ziqi Wang, Junyi Du, Leonardo Neves, and Xiang Ren · 2020
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Learning from explanations with neural execution tree
Ziqi Wang, Yujia Qin, Wenxuan Zhou, Jun Yan, Qinyuan Ye, Leonardo Neves, Zhiyuan Liu, and Xiang Ren · 2020
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Expbert: Representation engineering with natural language explanations
Shikhar Murty, Pang Wei Koh, and Percy Liang · 2020
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How much knowledge can you pack into the parameters of a language model?
Adam Roberts, Colin Raffel, and Noam Shazeer · 2020
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Electra: Pre-training text encoders as discriminators rather than generators
Kevin Clark, Minh-Thang Luong, Quoc V Le, and Christopher D Manning · 2020
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Bert-of-theseus: Compressing bert by progressive module replacing
Canwen Xu, Wangchunshu Zhou, Tao Ge, Furu Wei, and Ming Zhou · 2020
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Deebert: Dynamic early exiting for accelerating bert inference
Ji Xin, Raphael Tang, Jaejun Lee, Yaoliang Yu, and Jimmy Lin · 2020
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