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The ability of transformers to perform precision tasks such as question answering, Natural Language Inference (NLI) or summarising, have enabled them to be ranked as one of the best paradigm to address Natural Language Processing (NLP) tasks.
A sick cure for the evaluation of compositional distributional semantic models
Marco Marelli, Stefano Menini, Marco Baroni, Luisa Bentivogli, Raffaella Bernardi, and Roberto Zamparelli · 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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Adam: A method for stochastic optimization
DP Kingma, LJ Ba, et al · 2015
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A decomposable attention model for natural language inference
Ankur P Parikh, Oscar Täckström, Dipanjan Das, and Jakob Uszkoreit · 2016
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Supervised learning of universal sentence representations from natural language inference data
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loïc Barrault, and Antoine Bordes · 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 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Cyclical learning rates for training neural networks
Leslie N Smith · 2017
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Learning general purpose distributed sentence representations via large scale multi-task learning
Sandeep Subramanian, Adam Trischler, Yoshua Bengio, and Christopher J Pal · 2018
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Multi-reward reinforced summarization with saliency and entailment
Ramakanth Pasunuru and Mohit Bansal · 2018
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Ruse: Regressor using sentence embeddings for automatic machine translation evaluation
Hiroki Shimanaka, Tomoyuki Kajiwara, and Mamoru Komachi · 2018
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Compare, compress and propagate: Enhancing neural architectures with alignment factorization for natural language inference
Yi Tay, Anh Tuan Luu, and Siu Cheung Hui · 2018
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e-snli: natural language inference with natural language explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom · 2018
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Lessons from natural language inference in the clinical domain
Alexey Romanov and Chaitanya Shivade · 2018
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Neural natural language inference models enhanced with external knowledge
Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Diana Inkpen, and Si Wei · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Stochastic answer networks for natural language inference
Xiaodong Liu, Kevin Duh, and Jianfeng Gao · 2018
Cited alongside, same era.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman · 2018
Cited alongside, same era.
Xnli: Evaluating cross-lingual sentence representations
Alexis Conneau, Ruty Rinott, Guillaume Lample, Adina Williams, Samuel R. Bowman, Holger Schwenk, and Veselin Stoyanov · 2018
Cited alongside, same era.
Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman · 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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Cross-lingual language model pretraining
Guillaume Lample and Alexis Conneau · 2019
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Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks
Haoyang Huang, Yaobo Liang, Nan Duan, Ming Gong, Linjun Shou, Daxin Jiang, and Ming Zhou · 2019
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Unipi-nle at checkthat! 2020: Approaching fact checking from a sentence similarity perspective through the lens of transformers
Lucia Passaro, Alessandro Bondielli, Alessandro Lenci, and Francesco Marcelloni · 2020
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Repurposing entailment for multi-hop question answering tasks
Harsh Trivedi, Heeyoung Kwon, Tushar Khot, Ashish Sabharwal, and Niranjan Balasubramanian · 2019
Cited alongside, same era.
Bert with history answer embedding for conversational question answering
Chen Qu, Liu Yang, Minghui Qiu, W Bruce Croft, Yongfeng Zhang, and Mohit Iyyer · 2019
Cited alongside, same era.
Text summarization with pretrained encoders
Yang Liu and Mirella Lapata · 2019
Cited alongside, same era.
Enhancing unsupervised pretraining with external knowledge for natural language inference
Xiaoyu Yang, Xiaodan Zhu, Huasha Zhao, Qiong Zhang, and Yufei Feng · 2019
Cited alongside, same era.
Don’t take the premise for granted: Mitigating artifacts in natural language inference
Yonatan Belinkov, Adam Poliak, Stuart M Shieber, Benjamin Van Durme, and Alexander M Rush · 2019
Cited alongside, same era.
Unlearn dataset bias in natural language inference by fitting the residual
He He, Sheng Zha, and Haohan Wang · 2019
Cited alongside, same era.
Dialogue natural language inference
Sean Welleck, Jason Weston, Arthur Szlam, and Kyunghyun Cho · 2019
Cited alongside, same era.
Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Édouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov · 2020
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Nile: Natural language inference with faithful natural language explanations
Sawan Kumar and Partha Talukdar · 2020
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Farstail: A persian natural language inference dataset
Hossein Amirkhani, Mohammad Azari Jafari, Azadeh Amirak, Zohreh Pourjafari, Soroush Faridan Jahromi, and Zeinab Kouhkan · 2020
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Ocnli: Original chinese natural language inference
Hai Hu, Kyle Richardson, Liang Xu, Lu Li, Sandra Kübler, and Lawrence S Moss · 2020
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Youwei Song, Jiahai Wang, Zhiwei Liang, Zhiyue Liu, and Tao Jiang · 2020
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Calibration of pre-trained transformers
Shrey Desai and Greg Durrett · 2020
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Stress test evaluation of transformer-based models in natural language understanding tasks
Carlos Aspillaga, Andrés Carvallo, and Vladimir Araujo · 2020
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Filter: An enhanced fusion method for cross-lingual language understanding
Yuwei Fang, Shuohang Wang, Zhe Gan, Siqi Sun, and Jingjing Liu · 2020
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Better fine-tuning by reducing representational collapse
Armen Aghajanyan, Akshat Shrivastava, Anchit Gupta, Naman Goyal, Luke Zettlemoyer, and Sonal Gupta · 2020
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