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	Text augmentation preserving persona speech style and vocabulary</div>

Anastasia A. Matveeva, Makhnytkina Olesia V.

2023 , VOLUME 23, NUMBER 4 ( july-august )

ISSN 2226-1494 (print), ISSN 2500-0373 (online)

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Nikiforov
Vladimir O.
D.Sc., Prof.

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doi: 10.17586/2226-1494-2023-23-4-743-749

Text augmentation preserving persona speech style and vocabulary

A. A. Matveeva, O. V. Makhnytkina

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Article in Russian

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Matveeva A.A., Makhnytkina O.V. Text augmentation preserving persona speech style and vocabulary. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2023, vol. 23, no. 4, pp. 743–749 (in Russian). doi: 10.17586/2226-1494-2023-23-4-743-749

Abstract

Currently, various natural language processing tasks often require large data sets. However, for many tasks, collecting large datasets is quite tedious and expensive, and requires the involvement of experts. An increase in the amount of data can be achieved using methods of data augmentation, however, the use of classical approaches can lead to the inclusion of phrases in the data corpus that differ in the speech style and vocabulary of the target person, which can lead to both a change in the target class as well as the appearance of replicas with unnatural vocabulary use and lack of meaning. In this context, a new method for test data enrichment is proposed that takes into account the person’s style and vocabulary. In this article, a new method for expanding text data that preserves individual language features and vocabulary is proposed. The core of the method is to create individual templates for each person based on the analysis of syntactic trees of propositions and then to create new replicas according to the generated templates. The method was tested on the task of assessing the user’s emotional state in a dialogue. The search was carried out for data sets in English and Russian. The proposed method made it possible to improve the quality of solving these problems for both the English and Russian languages. Up to a 2 % increase in accuracy and weighted F1 metrics has been noted for various models. The results of the work can be applied to improve the accuracy and weighted F1 metrics of models designed to solve various problems for the English and Russian languages.

Keywords: text data augmentation, emotion recognition, statement valence evaluation

Acknowledgements. This research was supported by a grant from the Russian Science Foundation (22-11-00128 https://www.rscf.ru/ project/22-11-00128/).

References

Giridhara P.K., Mishra C., Venkataramana R.K., Bukhari S.S., Dengel A.R. A study of various text augmentation techniques for relation classification in free text. Proc. of the 8^th International Conference on Pattern Recognition Applications and Methods, 2019, pp. 360–367 https://doi.org/10.5220/0007311003600367
Papadaki M. Data Augmentation Techniques for Legal Text Analytics. A thesis submitted to Athens University of Economics and Business in fulfillment of the requirements for the degree of Master in Data Science, 2017, 33 p.
Zhang Z., Zweigenbaum P. GNEG: Graph-based negative sampling for word2vec. Proc. of the 56^th Annual Meeting of the Association for Computational Linguistics. V. 2, 2018, pp. 566–571. https://doi.org/10.18653/v1/P18-2090
Wei J., Zou K. EDA: Easy data augmentation techniques for boosting performance on text classification tasks. Proc. of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9^th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2018, pp. 6382–6388. https://doi.org/10.18653/v1/D19-1670
Wu X., Xia Y., Zhu J., Wu L., Xie S., Fan Y., Qin T. mixSeq: A simple data augmentation method for neural machine translation. Proc. of the 18^th International Conference on Spoken Language Translation (IWSLT 2021), 2021, pp. 192–197. https://doi.org/10.18653/v1/2021.iwslt-1.23
Kumar V., Choudhary A., Cho E. Data augmentation using pre-trained transformer models. Proc. of the 2^nd Workshop on Life-long Learning for Spoken Language Systems, 2020, pp. 18–26.
Kobayashi S. Contextual augmentation: Data augmentation by words with paradigmatic relations. Proc. of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), 2018, pp. 452–457. https://doi.org/10.18653/v1/N18-2072
Yu A., Dohan D., Luong M., Zhao R., Chen K., Norouzi M., Le Q. QANet: Combining local convolution with global self-attention for reading comprehension. Proc. of the ICLR Conference, 2018.
Mehdi R., Meyer M., Goutal S. Text Data Augmentation: Towards better detection of spear-phishing emails. arXiv, 2020, arXiv:2007.02033. https://doi.org/10.48550/arXiv.2007.02033
Edunov S., Ott M., Auli M., Grangier D. Understanding back-translation at scale. Proc. of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018, pp. 489–500. https://doi.org/10.18653/v1/D18-1045
Guo H., Mao Y., Zhang R. Augmenting data with mixup for sentence classification: An empirical study. arXiv, 2019, arXiv:1905.08941. https://doi.org/10.48550/arXiv.1905.08941
Coulombe C. Text data augmentation made simple by leveraging NLP cloud APIs. arXiv, 2018,arXiv:1812.04718. https://doi.org/10.48550/arXiv.1812.04718
Shen T., Lei T., Barzilay R., Jaakkola T. Style transfer from non-parallel text by cross-alignment. Advances in Neural Information Processing Systems, 2017, vol. 30.
Yang S., Huang X., Lau J.H., Erfani S. Robust task-oriented dialogue generation with contrastive pre-training and adversarial filtering. Findings of the Association for Computational Linguistics (EMNLP 2022), 2022, pp. 1220–1234.
Kovriguina L., Shilin I., Shipilo A., Putintseva A. Russian tagging and dependency parsing models for stanford CoreNLP natural language toolkit. Communications in Computer and Information Science, 2017, vol. 786, pp. 101–111. https://doi.org/10.1007/978-3-319-69548-8_8
Matveev Y., Matveev A., Frolova O., Lyakso E., Ruban N. Automatic speech emotion recognition of younger school age children. Mathematics, 2022, vol. 10, no. 14, pp. 2373. https://doi.org/10.3390/math10142373
Lyakso E., Frolova O., Matveev A., Matveev Y., Grigorev A., Makhnytkina O., Ruban N. Recognition of the emotional state of children with down syndrome by video, audio and text modalities: human and automatic. Lecture Notes in Computer Science, 2022, vol. 13721, pp. 438–450. https://doi.org/10.1007/978-3-031-20980-2_38
Kim T., Vossen P. EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa. arXiv, 2021, arXiv:2108.12009. https://doi.org/10.48550/arXiv.2108.12009
Song X., Zang L., Zhang R., Hu S., Huang L. Emotionflow: Capture the dialogue level emotion transitions. Proc. of the ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 8542–8546. https://doi.org/10.1109/ICASSP43922.2022.9746464
Shen W., Chen J., Quan X., Xie Z. DialogXL: All-in-One XLNet for multi-party conversation emotion recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 2021, vol. 35, no. 15, pp. 13789–13797 https://doi.org/10.1609/aaai.v35i15.17625
Shen W., Wu S., Yang Y., Quan X. Directed acyclic graph network for conversational emotion recognition. Proc. of the 59^th Annual Meeting of the Association for Computational Linguistics and the 11^th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 2021, pp. 1551–1560.

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