Abstract
Unsupervised text simplification has attracted much attention due to the scarcity of high-quality parallel text simplification corpora. Recent an unsupervised statistical text simplification based on phrase-based machine translation system (UnsupPBMT) achieved good performance, which initializes the phrase tables using the similar words obtained by word embedding modeling. Since word embedding modeling only considers the relevance between words, the phrase table in UnsupPBMT contains a lot of dissimilar words. In this paper, we propose an unsupervised statistical text simplification using pre-trained language modeling BERT for initialization. Specifically, we use BERT as a general linguistic knowledge base for predicting similar words. Experimental results show that our method outperforms the state-of-the-art unsupervised text simplification methods on three benchmarks, even outperforms some supervised baselines.
Similar content being viewed by others
References
Martin L, de la Clergerie É, Sagot B, Bordes A. Controllable sentence simplification. In: Proceedings of the 12th Conference on Language Resources and Evaluation. 2020, 4689–4698
Nisioi S, Štajner S, Ponzetto S P, Dinu L P. Exploring neural text simplification models. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. 2017, 85–91
Wubben S, van den Bosch A, Krahmer E. Sentence simplification by monolingual machine translation. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers. 2012, 1015–1024
Xu W, Napoles C, Pavlick E, Chen Q, Callison-Burch C. Optimizing statistical machine translation for text simplification. Transactions of the Association for Computational Linguistics, 2016, 4: 401–415
Zhang X, Lapata M. Sentence simplification with deep reinforcement learning. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017, 584–594
Zhu Z, Bernhard D, Gurevych I. A monolingual tree-based translation model for sentence simplification. In: Proceedings of the 23rd International Conference on Computational Linguistics. 2010, 1353–1361
Xu W, Callison-Burch C, Napoles C. Problems in current text simplification research: new data can help. Transactions of the Association for Computational Linguistics, 2015, 3: 283–297
Surya S, Mishra A, Laha A, Jain P, Sankaranarayanan K. Unsupervised neural text simplification. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019, 2058–2068
Kumar D, Mou L, Golab L, Vechtomova O. Iterative edit-based unsupervised sentence simplification. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020, 7918–7928
Qiang J, Wu X. Unsupervised statistical text simplification. IEEE Transactions on Knowledge and Data Engineering, 2021, 33(4): 1802–1806
Meng Y, Zhang Y, Huang J, Xiong C, Ji H, Zhang C, Han J. Text classification using label names only: a language model self-training approach. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020, 9006–9017
Petroni F, Rocktäschel T, Lewis P, Bakhtin A, Wu Y, Miller A H, Riedel S. Language models as knowledge bases?. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. 2019, 2463–2473
Roberts A, Raffel C, Shazeer N. How much knowledge can you pack into the parameters of a language model?. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020, 5418–5426
Zhang H, Khashabi D, Song Y, Roth D. TransOMCS: from linguistic graphs to commonsense knowledge. In: Proceedings of the 29th International Joint Conference on Artificial Intelligence. 2020, 4004–4010
Koehn P, Hoang H, Birch A, Callison-Burch C, Federico M, Bertoldi N, Cowan B, Shen W, Moran C, Zens R, Dyer C, Bojar O, Constantin A, Herbst E. Moses: open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions. 2007, 177–180
Artetxe M, Labaka G, Agirre E, Cho K. Unsupervised neural machine translation. In: Proceedings of the 6th International Conference on Learning Representations. 2018
Pennington J, Socher R, Manning C. GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2014, 1532–1543
Farr J N, Jenkins J J, Paterson D G. Simplification of flesch reading ease formula. Journal of Applied Psychology, 1951, 35(5): 333–337
Heafield K. KenLM: faster and smaller language model queries. In: Proceedings of the 6th Workshop on Statistical Machine Translation. 2011, 187–197
Lample G, Ott M, Conneau A, Denoyer L, Ranzato M. Phrase-based & neural unsupervised machine translation. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018, 5039–5049
Li D, Zhang Y, Peng H, Chen L, Brockett C, Sun M T, Dolan B. Contextualized perturbation for textual adversarial attack. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021, 5053–5069
Glavaš G, Štajner S. Simplifying lexical simplification: do we need simplified corpora?. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. 2015, 63–68
Brysbaert M, New B. Moving beyond Kučera and Francis: a critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English. Behavior Research Methods, 2009, 41(4): 977–990
Qiang J, Li Y, Zhu Y, Yuan Y, Wu X. Lexical simplification with pretrained encoders. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(5): 8649–8656
Qiang J, Lv X, Li Y, Yuan Y, Wu X. Chinese lexical simplification. IEEE/ACV Transactions on Audio, Speech, and Language Processing, 2021, 29: 1819–1828
Zhao S, Meng R, He D, Andi S, Bambang P. Integrating transformer and paraphrase rules for sentence simplification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018, 3164–3173
Narayan S, Gardent C. Hybrid simplification using deep semantics and machine translation. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. 2014, 435–445
Guo H, Pasunuru R, Bansal M. Dynamic multi-level multi-task learning for sentence simplification. In: Proceedings of the 27th International Conference on Computational Linguistics. 2018, 462–476
Dong Y, Li Z, Rezagholizadeh M, Cheung J C K. EditNTS: an neural programmer-interpreter model for sentence simplification through explicit editing. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019, 3393–3402
Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I. Language models are unsupervised multitask learners. OpenAI Blog, 2019, 1(8): 9
Yang Z, Dai Z, Yang Y, Carbonell J, Salakhutdinov R, Le Q V. XLNet: generalized autoregressive pretraining for language understanding. In: Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). 2019, 5754–5764
Devlin J, Chang M W, Lee K, Toutanova K. BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1. 2019, 4171–4186
Lan Z, Chen M, Goodman S, Gimpel K, Sharma P, Soricut R. ALBERT: a lite BERT for self-supervised learning of language representations. In: Proceedings of the 8th International Conference on Learning Representations. 2020
Lewis M, Liu Y, Goyal N, Ghazvininejad M, Mohamed A, Levy O, Stoyanov V, Zettlemoyer L. BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2019, 7871–7880
Scarton C, Specia L. Learning simplifications for specific target audiences. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. 2018, 712–718
Narayan S, Gardent C. Unsupervised sentence simplification using deep semantics. In: Proceedings of the 9th International Natural Language Generation Conference. 2015, 111–120
Martin L, Fan A, de la Clergerie É, Bordes A, Sagot B. MUSS: multilingual unsupervised sentence simplification by mining paraphrases. 2021, arXiv preprint arXiv: 2005.00352
Artetxe M, Labaka G, Agirre E. Unsupervised statistical machine translation. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018, 3632–3642
Wenzek G, Lachaux M A, Conneau A, Chaudhary V, Guzmán F, Joulin A, Grave E. CCNET: extracting high quality monolingual datasets from web crawl data. In: Proceedings of the 12th Language Resources and Evaluation Conference. 2020, 4003–4012
Pavlick E, Callison-Burch C. Simple PPDB: a paraphrase database for simplification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. 2016, 143–148
Acknowledgements
This research was partially supported by the National Natural Science Foundation of China (Grant Nos. 62076217 and 61906060); and the Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT) of the Ministry of Education, China (IRT17R32).
Author information
Authors and Affiliations
Corresponding authors
Additional information
Jipeng Qiang is currently an associate professor in the School of Information Engineering, at Yangzhou University, China. He received his PhD degree in computer science and technology from Hefei University of Technology, China in 2016. He was a PhD visiting student in the Artificial Intelligence Lab at the University of Massachusetts Boston, USA from 2014 to 2016. He has published more than 50 papers, including AAAI, EMNLP, TKDE, TASLP, and TKDD. His research interests mainly include natural language processing and data mining.
Feng Zhang is currently working toward the MS degree of computer science at the Yangzhou University, China. He received his BS degree in computer science from Huaiyin Institute of Technology, China. His research interest is text simplification.
Yun Li is currently a professor in the School of Information Engineering, Yangzhou University, China. He received the MS degree in computer science and technology from Hefei University of Technology, China in 1991, and the PhD degree in control theory and control engineering from Shanghai University, China in 2005. He has
Yunhao Yuan is currently an associate professor in the School of Information Engineering, Yangzhou University, China. He received the MEng degree in computer science and technology from Yangzhou University, China, in 2009, and the PhD degree in pattern recognition and intelligence system from Nanjing University of Science and Technology, China in 2013. His research interests include pattern recognition, data mining, and image processing.
Yi Zhu is currently an assistant professor in the School of Information Engineering, Yangzhou University, China. He received the BS degree from Anhui University, the MS degree from the University of Science and Technology of China, and the PhD degree from Hefei University of Technology, China. His research interests are in data mining and knowledge engineering. His research interests include data mining and recommendation systems.
Xindong Wu is a professor in the School of Computer Science and Information Engineering at the Hefei University of Technology, China, and the president of Mininglamp Academy of Sciences, Mininglamp, China, and a fellow of IEEE and AAAS. He received his BS and MS degrees in computer science from the Hefei University of Technology, China, and his PhD degree in artificial intelligence from the University of Edinburgh, UK. His research interests include data mining, big data analytics, and knowledge-based systems.
Electronic supplementary material
Rights and permissions
About this article
Cite this article
Qiang, J., Zhang, F., Li, Y. et al. Unsupervised statistical text simplification using pre-trained language modeling for initialization. Front. Comput. Sci. 17, 171303 (2023). https://doi.org/10.1007/s11704-022-1244-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11704-022-1244-0