中文礼貌风格迁移的研究

朱洪坤,左家莉,何思兰,曾雪强,王明文

PDF(2069 KB)
PDF(2069 KB)
中文信息学报 ›› 2023, Vol. 37 ›› Issue (12) : 146-154.
情感分析与社会计算

中文礼貌风格迁移的研究

  • 朱洪坤,左家莉,何思兰,曾雪强,王明文
作者信息 +

Research on Chinese Politeness Style Transfer

  • ZHU Hongkun, ZUO Jiali, HE Silan, ZENG Xueqiang, WANG Mingwen
Author information +
History +

摘要

该文研究了一个关于中文的礼貌迁移任务,该任务旨在保留原始文本的内容和意义的同时,将非礼貌的文本转换为礼貌的文本。针对这个任务,建设了一个中文礼貌风格迁移的语料库。并基于此,构建了结合文本对齐模块和流畅度评估模块的中文礼貌风格迁移模型,文本对齐模块在保证文本风格迁移的同时保留文本的内容,流畅度评估模块可以提升生成文本的流畅度。在中文语料库和英文语料库的实验表明,该方法在内容保存度和流畅度这两个指标上都有较强的竞争力。

Abstract

This paper studies the politeness transfer task in Chinese, which aims to convert non-polite texts into polite text while preserving the content and the original text meaning. For this task, the paper constructs a Chinese politeness style transfer corpus. Based on this, the paper constructs a Chinese politeness style transfer model that combines the text alignment module and the fluency evaluation module. The text alignment module can ensure the text style transfer while retaining the text content, and the fluency evaluation module can improve the fluency of the generated text. Experiments on Chinese corpus and English corpus show that the method has strong competitiveness in content preservation and fluency.

关键词

文本风格迁移 / 礼貌迁移 / 内容保存度 / 流畅度

Key words

text style transfer / politeness transfer / content preservation / fluency

引用本文

导出引用
朱洪坤,左家莉,何思兰,曾雪强,王明文. 中文礼貌风格迁移的研究. 中文信息学报. 2023, 37(12): 146-154
ZHU Hongkun, ZUO Jiali, HE Silan, ZENG Xueqiang, WANG Mingwen. Research on Chinese Politeness Style Transfer. Journal of Chinese Information Processing. 2023, 37(12): 146-154

参考文献

[1] SHEN T, LEI T,BARZILAY R, et al. Style transfer from non-parallel text by cross-alignment[C]//Proceedings of the Advances in Neural Information Processing Systems, 2017: 6833-6844.
[2] RAO S, TETREAULT J. Dear sir or madam, may i introduce the gyafc dataset: Corpus, bench-marks and metrics for formality style transfer[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2018: 129-140.
[3] MADAAN A, SETLUR A, PAREKH T, et al. Politeness transfer: A tag and generate approach[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020: 1869-1881.
[4] KANG D, HOVY E. Style is not a single variable: Case studies for cross-style language under-standing[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021, 1: 2376-2387.
[5] JIN D, JIN Z, HU Z, et al. Deep learning for text style transfer: A survey[C]//Proceedings of the Computational Linguistics, 2022, 48(1): 155-205.
[6] DANESCU NICULESCU MIZIL C, SUDHOF M, JURAFSKY D, et al. A computational approach to politeness with application to social factors[C]//Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, 2013: 250-259.
[7] MEIER A J. Defining politeness: Universality in appropriateness[J]. Language Sciences, 1995, 17(4): 345-356.
[8] BROWN P, LEVINSON S C, LEVINSON S C. Politeness: Some universals in language usage[M]. Cambridge University Press, 1987.
[9] PRABHUMOYE S, TSVETKOV Y, SALAKHUTDINOV R, et al. Style transfer through back-translation[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018: 866-876.
[10] HUANG F, CHEN Z, WU C H, et al. NAST: A non-autoregressive generator with word alignment for unsupervised text style transfer[C]// Proceedings of the Association for Computational Linguistics: ACL-IJCNLP, 2021: 1577-1590.
[11] LIU A, WANG A, OKAZAKI N. Semi-supervised formality style transfer with consistency training[C]//Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 2022: 4689-4701.
[12] FU Z, TAN X, PENG N, et al. Style transfer in text: Exploration and evaluation[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2018.
[13] YANG Z, HU Z, DYER C, et al. Unsupervised text style transfer using language models as discriminators[J]. Advances in Neural Information Processing Systems, 2018, 31: 7298-7309.
[14] JOHN V,MOU L, BAHULEYAN H, et al. Disentangled representation learning for non-parallel text style transfer[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019: 424-434.
[15] LI J, JIA R, HE H, et al. Delete, retrieve, generate: a simple approach to sentiment and style transfer[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2018: 1865-1874.
[16] SUDHAKAR A, UPADHYAY B, MAHESWARAN A. Transforming delete, retrieve, generate approach for controlled text style transfer[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019: 3269-3279.
[17] NIU T, BANSAL M. Polite dialogue generation without parallel data[J]. Transactions of the Association for Computational Linguistics, 2018, 6: 373-389.
[18] VASWANI A,SHAZEER N, PARMAR N, et al. Attention is all you need[J]. Advances in Neural Information Processing Systems, 2017, 30: 6000-6010.
[19] LIN T, WANG Y, LIU X, et al. A survey of transformers[J]. arXiv preprint arXiv:2106:04554.
[20] KARITA S, CHEN N, HAYASHI T, et al. A comparative study on transformer vs rnn in speech applications[C]//Proceedings of the IEEE Automatic Speech Recognition and Understanding Workshop, 2019: 449-456.
[21] DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16x16 words: Transformers for image recognition at scale[C]//Proceedings of the International Conference on Learning Representations, 2021.
[22] HAN X, ZHANG Z, DING N, et al. Pre-trained models: past, present and future[J]. AI Open, 2021, 2: 225-250.
[23] LI B, HOU Y, CHE W. Data augmentation approaches in natural language processing: A survey[J]. AI Open, 2022, 3: 71-90.
[24] SUTSKEVER I, VINYALS O, LE Q V. Sequence to sequence learning with neural networks[J]. Advances in Neural Information Processing Systems, 2014, 27: 3104-3112.
[25] BAHDANAU D, CHO K, BENGIO Y. Neural machine translation by jointly learning to align and translate[C]//Proceedings of ICLR 2015, 2015: 1-15.
[26] ZHANG Y, GE T, SUN X. Parallel data augmentation for formality style transfer[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020: 3221-3228.
[27] KLIMT B, YANG Y. Introducing the enron corpus[C]//Proceedings of the 5th CEAS, 2004.
[28] FENG Y,XIE W, GU S, et al. Modeling fluency and faithfulness for diverse neural machine translation[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(1): 59-66.
[29] VOIGT R, JURGENS D, PRABHAKARAN V, et al. RtGender: A corpus for studying differential responses to gender[C]//Proceedings of the 11th International Conference on Language Resources and Evaluation, 2018: 2814-2820.
[30] SRIVASTAVA N, HINTON G,KRIZHEVSKY A, et al. Dropout: A simple way to prevent neural networks from overfitting[J]. The Journal of Machine Learning Research, 2014, 15(1): 1929-1958.
[31] SUN J. Jieba Chinese word segmentation tool.[CP/OL]. https://github.com/fxsjy/jieba, 2012.
[32] SENNRICH R, HADDOW B, BIRCH A. Neural machine translation of rare words with subword units[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2016, 1: 1715-1725.
[33] PAPINENI K, ROUKOS S, WARD T, et al. Bleu: A method for automatic evaluation of machine translation[C]//Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, 2002: 311-318.

基金

国家自然科学基金(61866018,62266021)
PDF(2069 KB)

Accesses

Citation

Detail

段落导航
相关文章

/