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Randomly masking text spans in ordinary texts in the pre-training stage hardly allows models to acquire the ability to generate simple texts.
Dict-bert: Enhancing language model pre-training with dictionary
Wenhao Yu, Chenguang Zhu, Yuwei Fang, Donghan Yu, Shuohang Wang, Yichong Xu, Michael Zeng, and Meng Jiang. 2022 · 1918
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Lsbert: A simple framework for lexical simplification
Jipeng Qiang, Yun Li, Yi Zhu, Yunhao Yuan, and Xindong Wu. 2020b · 2006
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Learning a lexical simplifier using wikipedia
Colby Horn, Cathryn Manduca, and David Kauchak. 2014 · 2014
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Problems in current text simplification research: New data can help
Wei Xu, Chris Callison-Burch, and Courtney Napoles. 2015 · 2015
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Benchmarking lexical simplification systems
Gustavo Paetzold and Lucia Specia. 2016 · 2016
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Optimizing statistical machine translation for text simplification
Wei Xu, Courtney Napoles, Ellie Pavlick, Quanze Chen, and Chris Callison-Burch. 2016 · 2016
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Sentence simplification with deep reinforcement learning
Xingxing Zhang and Mirella Lapata. 2017 · 2017
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A word-complexity lexicon and a neural readability ranking model for lexical simplification
Mounica Maddela and Wei Xu. 2018 · 2018
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Bleu is not suitable for the evaluation of text simplification
Elior Sulem, Omri Abend, and Ari Rappoport. 2018 · 2018
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Editnts: An neural programmer-interpreter model for sentence simplification through explicit editing
Yue Dong, Zichao Li, Mehdi Rezagholizadeh, and Jackie Chi Kit Cheung. 2019 · 2019
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Recursive context-aware lexical simplification
Sian Gooding and Ekaterina Kochmar. 2019 · 2019
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Text summarization with pretrained encoders
Yang Liu and Mirella Lapata. 2019 · 2019
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fairseq: A fast, extensible toolkit for sequence modeling
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019 · 2019
Cited alongside, same era.
Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019 · 2019
Cited alongside, same era.
Data-driven sentence simplification: Survey and benchmark
Fernando Alva-Manchego, Carolina Scarton, and Lucia Specia. 2020 · 2020
Cited alongside, same era.
Train no evil: Selective masking for task-guided pre-training
Yuxian Gu, Zhengyan Zhang, Xiaozhi Wang, Zhiyuan Liu, and Maosong Sun. 2020 · 2020
Cited alongside, same era.
Don’t stop pretraining: Adapt language models to domains and tasks
Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A Smith. 2020 · 2020
Cited alongside, same era.
Neural crf model for sentence alignment in text simplification
Controllable text simplification with explicit paraphrasing
Mounica Maddela, Fernando Alva-Manchego, and Wei Xu. 2021 · 2021
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Text simplification by tagging
Kostiantyn Omelianchuk, Vipul Raheja, and Oleksandr Skurzhanskyi. 2021 · 2021
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Utfpr at semeval-2021 task 1: Complexity prediction by combining bert vectors and classic features
Gustavo Paetzold. 2021 · 2021
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Deepblueai at semeval-2021 task 1: Lexical complexity prediction with a deep ensemble approach
Chunguang Pan, Bingyan Song, Shengguang Wang, and Zhipeng Luo. 2021 · 2021
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Semeval-2021 task 1: Lexical complexity prediction
Matthew Shardlow, Richard Evans, Gustavo Paetzold, and Marcos Zampieri. 2021 · 2021
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Document-level text simplification: Dataset, criteria and baseline
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Chao Jiang, Mounica Maddela, Wuwei Lan, Yang Zhong, and Wei Xu. 2020 · 2020
Cited alongside, same era.
Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J Liu, et al. 2020 · 2020
Cited alongside, same era.
Complex—a new corpus for lexical complexity prediction from likert scale data
Matthew Shardlow, Michael Cooper, and Marcos Zampieri. 2020 · 2020
Cited alongside, same era.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. 2020 · 2020
Cited alongside, same era.
The (un) suitability of automatic evaluation metrics for text simplification
Fernando Alva-Manchego, Carolina Scarton, and Lucia Specia. 2021 · 2021
Cited alongside, same era.
Keep it simple: Unsupervised simplification of multi-paragraph text
Philippe Laban, Tobias Schnabel, Paul Bennett, and Marti A Hearst. 2021 · 2021
Cited alongside, same era.
Renliang Sun, Hanqi Jin, and Xiaojun Wan. 2021 · 2021
Later among the works it cites.
Flesch-kincaid is not a text simplification evaluation metric
Teerapaun Tanprasert and David Kauchak. 2021 · 2021
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2022 · 2022
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Evaluating factuality in text simplification
Ashwin Devaraj, William Sheffield, Byron C Wallace, and Junyi Jessy Li. 2022 · 2022
Later among the works it cites.
Deep: Denoising entity pre-training for neural machine translation
Junjie Hu, Hiroaki Hayashi, Kyunghyun Cho, and Graham Neubig. 2022 · 2022
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Exploiting summarization data to help text simplification
Renliang Sun, Zhixian Yang, and Xiaojun Wan. 2023 · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
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