Abstract
During the several years of production of an animated movie, review meetings take place daily, where supervisors and directors generate text notes about fixes needed for the movie. These notes are manually assigned to artistic departments for them to fixed. Being manual, many notes are not properly assigned and are never fixed, lowering the quality of the final movie. This paper presents a proposal for automating the distribution of these notes using multi-label text classification techniques. The comparison of the results obtained by fine-tuning several transformer-based language models is presented. A highest mean accuracy of 0.776 is achieved assigning several departments to each of the review notes in the test set with a BERT Multilingual model. A mean accuracy of 0.762 was reached in just 10 epochs and 10 min of training on an RTX-3090 with a DistilBERT transformer model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
It is important to differentiate the review notes of the production process from the reviews of finished movies carried out by users, such as, the Large Movie Review Dataset [11].
References
Chen, X., et al.: A survey of multi-label text classification based on deep learning. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds.) ICAIS 2022. LNCS, vol. 13338, pp. 443–456. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-06794-5_36
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Pre-training of deep bidirectional transformers for language understanding. In: naacL-HLT, pp. 4171–4186 (2019)
Feng, L., Senapati, J., Liu, B.: TaDaa: real time ticket assignment deep learning auto advisor for customer support, help desk, and issue ticketing systems. arXiv:2207.11187 (2022)
Gage, P.: A new algorithm for data compression. C Users J. 12(2), 23–38 (1994)
Garcés, D., Santos, M., Fernández-Llorca, D.: Text classification for automatic distribution of review notes in movie production. In: García Bringas, P., et al. (eds.) SOCO 2023. LNNS, vol. 749, pp. 3–12. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-42529-5_1
Gasparetto, A., Marcuzzo, M., Zangari, A., Albarelli, A.: A survey on text classification algorithms: from text to predictions. Information 13(2), 83 (2022)
Goštautaitė, D., Sakalauskas, L.: Multi-label classification and explanation methods for students’ learning style prediction and interpretation. Appl. Sci. 12(11), 5396 (2022)
Liu, Y., et al.: Summary of ChatGPT/GPT-4 research and perspective towards the future of large language models (2023)
Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. arXiv:1907.11692 (2019)
Llorella, F.R., Iáñez, E., Azorín, J.M., Patow, G.: Binary visual imagery discriminator from EEG signals based on convolutional neural networks. Rev. Iberoamericana Autom. Inform. Ind. 19(1), 108–116 (2022)
Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 142–150 (2011)
Martin, T.: The reuters dataset (2017). https://martin-thoma.com/nlp-reuters
Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., Gao, J.: Deep learning-based text classification: a comprehensive review. ACM Comput. Surv. 54(3), 1–40 (2021)
Penedo, G., et al.: The RefinedWeb dataset for falcon LLM: outperforming curated corpora with web data, and web data only. arXiv:2306.01116 (2023)
Pires, T., Schlinger, E., Garrette, D.: How multilingual is multilingual BERT? (2019)
Rahman, M., Akter, Y.: Topic classification from text using decision tree, K-NN and multinomial naïve bayes. In: 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT) (2019)
Sanh, V., Debut, L., Chaumond, J., Wolf, T.: DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv:1910.01108 (2019)
Schuster, M., Nakajima, K.: Japanese and Korean voice search. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5149–5152 (2012)
Touvron, H., et al.: LLaMA: open and efficient foundation language models. arXiv:2302.13971 (2023)
Tsoumakas, G., Katakis, I.: Multi-label classification: an overview. Int. J. Data Warehouse. Min. 3, 1–13 (2009)
Zhang, L., Wang, S., Liu, B.: Deep learning for sentiment analysis : a survey. WIREs Data Min. Knowl. Discov. 8(4), e1253 (2018)
Zhang, M.L., Zhou, Z.H.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819–1837 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Garcés, D., Santos, M., Fernández-Llorca, D. (2023). Language Models for Automatic Distribution of Review Notes in Movie Production. In: Quaresma, P., Camacho, D., Yin, H., Gonçalves, T., Julian, V., Tallón-Ballesteros, A.J. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2023. IDEAL 2023. Lecture Notes in Computer Science, vol 14404. Springer, Cham. https://doi.org/10.1007/978-3-031-48232-8_23
Download citation
DOI: https://doi.org/10.1007/978-3-031-48232-8_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-48231-1
Online ISBN: 978-3-031-48232-8
eBook Packages: Computer ScienceComputer Science (R0)