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Identifying words that impact a task's performance more than others is a challenge in natural language processing.
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“SentiGAN: Generating Sentimental Texts via Mixture Adversarial Networks.”
Ke Wang and Xiaojun Wan · 2018
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“SciBERT: A Pretrained Language Model for Scientific Text”
Iz Beltagy, Kyle Lo and Arman Cohan · 2019
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Kevin Clark, Urvashi Khandelwal, Omer Levy and Christopher Manning · 2019
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Colin Clement, Matthew Bierbaum and Kevin O’Keeffe · 2019
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“A structural probe for finding syntax in word representations”
John Hewitt and Christopher Manning · 2019
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“Attention is not Explanation”
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“Albert: A lite bert for self-supervised learning of language representations”
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“BioBERT: a pre-trained biomedical language representation model for biomedical text mining”
Jinhyuk Lee et al · 2019
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“Towards controllable and personalized review generation”
Pan Li and Alexander Tuzhilin · 2019
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“Open Sesame: getting inside BERT’s linguistic knowledge”
Yongjie Lin, Yi Tan and Robert Frank · 2019
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“Roberta: A robustly optimized bert pretraining approach”
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“ETC: Encoding long and structured inputs in transformers”
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“Exploring Conditional Text Generation for Aspect-Based Sentiment Analysis”
Siva Chebolu, Franck Dernoncourt, Nedim Lipka and Thamar Solorio · 2021
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