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Position embeddings, encoding the positional relationships among tokens in text sequences, make great contributions to modeling local context features in Transformer-based pre-trained language models.
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 · 1907
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Natural Language Processing with Python
Steven Bird, Ewan Klein, and Edward Loper. 2009 · 2009
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Adam: A method for stochastic optimization
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Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Matthew Dunn, Levent Sagun, Mike Higgins, V. Ugur Güney, Volkan Cirik, and Kyunghyun Cho. 2017 · 2017
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Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017 · 2017
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Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2017 · 2017
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Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Know what you don’t know: Unanswerable questions for SQuAD
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
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Contextualized word representations for reading comprehension
Shimi Salant and Jonathan Berant. 2018 · 2018
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What makes reading comprehension questions easier?
Saku Sugawara, Kentaro Inui, Satoshi Sekine, and Akiko Aizawa. 2018 · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018 · 2018
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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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MRQA 2019 shared task: Evaluating generalization in reading comprehension
Look at the first sentence: Position bias in question answering
Miyoung Ko, Jinhyuk Lee, Hyunjae Kim, Gangwoo Kim, and Jaewoo Kang. 2020 · 2020
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Global-to-local neural networks for document-level relation extraction
Difeng Wang, Wei Hu, Ermei Cao, and Weijian Sun. 2020 · 2020
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RoFormer: Enhanced transformer with rotary position embedding
Jianlin Su, Yu Lu, Shengfeng Pan, Bo Wen, and Yunfeng Liu. 2021 · 2021
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Document-level relation extraction as semantic segmentation
Ningyu Zhang, Xiang Chen, Xin Xie, Shumin Deng, Chuanqi Tan, Mosha Chen, Fei Huang, Luo Si, and Huajun Chen. 2021 · 2021
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Train short, test long: Attention with linear biases enables input length extrapolation
Ofir Press, Noah Smith, and Mike Lewis. 2022 · 2022
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Adam Fisch, Alon Talmor, Robin Jia, Minjoon Seo, Eunsol Choi, and Danqi Chen. 2019 · 2019
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Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
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Document-level relation extraction with adaptive thresholding and localized context pooling
Wenxuan Zhou, Kevin Huang, Tengyu Ma, and Jing Huang. 2021 · 2019
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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 · 2020
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SpanBERT: Improving pre-training by representing and predicting spans
Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S. Weld, Luke Zettlemoyer, and Omer Levy. 2020 · 2020
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Kazutoshi Shinoda, Saku Sugawara, and Akiko Aizawa. 2022 · 2022
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A length-extrapolatable transformer
Yutao Sun, Li Dong, Barun Patra, Shuming Ma, Shaohan Huang, Alon Benhaim, Vishrav Chaudhary, Xia Song, and Furu Wei. 2022 · 2022
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Qingyu Tan, Lu Xu, Lidong Bing, Hwee Tou Ng, and Sharifah Mahani Aljunied. 2022 · 2022
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Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2031
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