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Despite the recent advancement in NLP research, cross-lingual transfer for natural language generation is relatively understudied.
Three scenarios for continual learning
Gido M Van de Ven and Andreas S Tolias. 2019 · 1904
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Mike Lewis, Marjan Ghazvininejad, Gargi Ghosh, Armen Aghajanyan, Sida I. Wang, and Luke Zettlemoyer. 2020a · 2006
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Cross-language document summarization based on machine translation quality prediction
Xiaojun Wan, Huiying Li, and Jianguo Xiao. 2010 · 2010
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Zero-shot cross-lingual neural headline generation
Shi-qi Shen, Yun Chen, Cheng Yang, Zhi-yuan Liu, and Mao-song Sun. 2018 · 2018
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Paragraph-level neural question generation with maxout pointer and gated self-attention networks
Yao Zhao, Xiaochuan Ni, Yuanyuan Ding, and Qifa Ke. 2018 · 2018
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Zero-shot cross-lingual abstractive sentence summarization through teaching generation and attention
Xiangyu Duan, Mingming Yin, Min Zhang, Boxing Chen, and Weihua Luo. 2019 · 2019
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Improved zero-shot neural machine translation via ignoring spurious correlations
Jiatao Gu, Yong Wang, Kyunghyun Cho, and Victor O.K. Li. 2019 · 2019
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Multiple news headlines generation using page metadata
Kango Iwama and Yoshinobu Kano. 2019 · 2019
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Cross-lingual training for automatic question generation
Vishwajeet Kumar, Nitish Joshi, Arijit Mukherjee, Ganesh Ramakrishnan, and Preethi Jyothi. 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
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TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages
Jonathan H. Clark, Eunsol Choi, Michael Collins, Dan Garrette, Tom Kwiatkowski, Vitaly Nikolaev, and Jennimaria Palomaki. 2020 · 2020
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Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020 · 2020
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Xtreme: A massively multilingual multi-task benchmark for evaluating cross-lingual generalisation
Junjie Hu, Sebastian Ruder, Aditya Siddhant, Graham Neubig, Orhan Firat, and Melvin Johnson. 2020 · 2020
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WikiLingua: A new benchmark dataset for cross-lingual abstractive summarization
Faisal Ladhak, Esin Durmus, Claire Cardie, and Kathleen McKeown. 2020 · 2020
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Multilingual denoising pre-training for neural machine translation
Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, and Luke Zettlemoyer. 2020 · 2020
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Norio Takahashi, Tomohide Shibata, Daisuke Kawahara, and Sadao Kurohashi. 2019 · 2019
Cited alongside, same era.
Cross-lingual natural language generation via pre-training
Zewen Chi, Li Dong, Furu Wei, Wenhui Wang, Xian-Ling Mao, and Heyan Huang. 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. 2020b
Cited in the paper.
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. 2020c
Cited in the paper.
MLQA: Evaluating cross-lingual extractive question answering
Patrick Lewis, Barlas Oguz, Ruty Rinott, Sebastian Riedel, and Holger Schwenk. 2020d
Cited in the paper.
Later among the works it cites.
Learning to distract: A hierarchical multi-decoder network for automated generation of long distractors for multiple-choice questions for reading comprehension
Kaushal Kumar Maurya and Maunendra Sankar Desarkar. 2020 · 2020
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Bertscore: Evaluating text generation with BERT
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 2020
Later among the works it cites.