Cross-Lingual Semantic Alignment With Adaptive Transformer Models For Zero-Shot Text Categorization

Dr. Lin Mei; Huiqin Zhao

Authors

Dr. Lin Mei Department of Computational Linguistics, Peking University, Beijing, China
Huiqin Zhao College of Computer Science and Technology, Zhejiang University, Hangzhou, China

Keywords:

Multilingual transformers, zero-shot learning, cross-lingual transfer, text classification

Abstract

The global nature of information demands Artificial Intelligence (AI) systems capable of understanding and classifying text across multiple languages, even when labeled training data for a target language is unavailable. This scenario, known as zero-shot cross-lingual text classification, presents a significant challenge due to inherent linguistic divergence and data sparsity in many languages. Multilingual transformer models have emerged as foundational components for this task, pre-trained on diverse linguistic corpora to learn shared representations. However, achieving robust zero-shot transfer necessitates sophisticated techniques for semantic alignment across language barriers. This article explores how principles from unsupervised contrastive learning, a paradigm that has revolutionized multimodal representation learning, can be adapted to enhance multilingual transformers for zero-shot cross-lingual text categorization. We discuss the methodological foundations, highlighting how contrastive objectives can explicitly align semantic spaces across languages, thereby enabling more adaptive and effective cross-lingual transfer. By synthesizing insights from related work in multimodal alignment, we illustrate the potential for learning robust, transferable cross-lingual representations. Furthermore, we address the unique challenges in this cross-lingual context and outline critical future research directions towards building truly universal and data-efficient text classification systems.

References

He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020). Momentum Contrast for Unsupervised Visual Representation Learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 9726–9735.

Oord, A. v. d., Li, Y., & Vinyals, O. (2018). Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748.

Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. International Conference on Machine Learning, 1597–1607.

Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., ... & Sutskever, I. (2021). Learning transferable visual models from natural language supervision (CLIP). International Conference on Machine Learning, 8748–8763.

Li, J., Zhou, P., Xiong, C., & Hoi, S. C. H. (2020). Prototypical contrastive learning of unsupervised representations. arXiv preprint arXiv:2005.04966.

Yuan, L., Chen, Y., Wang, T., Yu, W., Shi, Y., Jiang, Z., ... & Gao, J. (2021). Multimodal contrastive training for visual representation learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10431–10441.

Nakada, R., Gulluk, H. I., Deng, Z., Ji, W., Zou, J., & Zhang, L. (2023). Understanding multimodal contrastive learning and incorporating unpaired data. Proceedings of Machine Learning Research, 206, 4348–4380.

Lin, Z., Zhang, Z., Wang, M., Shi, Y., & Wu, X. (2022). Multi-modal Contrastive Representation Learning for Entity Alignment. In Proceedings of COLING 2022, 2572–2584.

Alayrac, J. B., et al. (2022). FLAVA: A foundational language and vision alignment model. CVPR, 15638–15650.

Tsai, Y. H. H., Bai, S., Yamada, M., Morency, L. P., & Salakhutdinov, R. (2019). Multimodal transformer for unaligned multimodal language sequences. ACL, 6558–6569.

Chen, X., & He, K. (2021). Exploring simple Siamese representation learning. CVPR, 15750–15758.

Wei, H., Qi, P., & Ma, X. (2021). Cross-modal contrastive learning for multivariate time series. NeurIPS, 34, 23346–23357.

Miech, A., Alayrac, J. B., Smaira, L., Laptev, I., Sivic, J., & Zisserman, A. (2020). End-to-end learning of visual representations from uncurated instructional videos. CVPR, 9879–9889.

Arandjelović, R., & Zisserman, A. (2017). Look, listen and learn. ICCV, 609–617.

Grill, J. B., Strub, F., Altché, F., Tallec, C., Richemond, P. H., Buchatskaya, E., ... & Valko, M. (2020). Bootstrap your own latent: A new approach to self-supervised learning. NeurIPS, 33, 21271–21284.

Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., & Joulin, A. (2020). Unsupervised learning of visual features by contrasting cluster assignments. NeurIPS, 33.

Tian, Y., Krishnan, D., & Isola, P. (2020). Contrastive multiview coding. ECCV, 776–794.

He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020). Momentum Contrast for Unsupervised Visual Representation Learning. IEEE CVPR.

Zhai, X., Mustafa, B., Kolesnikov, A., & Beyer, L. (2023). Sigmoid CLIP: An improved contrastive loss for language-image pretraining. NeurIPS Workshop.

Hu, Z., Ma, X., Liu, Z., Hovy, E., & Xing, E. P. (2016). Harnessing deep neural networks with logic rules. ACL, 2410–2420.

Guo, W., Wang, J., & Wang, S. (2019). Deep Multimodal Representation Learning: A Survey. IEEE Access.

Poklukar, G., et al. (2022). Geometric multimodal contrastive representation learning. Proceedings of Machine Learning Research, 162, 1–20.

Pinheiro, P. O., Almahairi, A., Benmalek, R. Y., Golemo, F., & Courville, A. (2020). Unsupervised learning of dense visual representations. arXiv preprint arXiv:2011.05499.

Lin, Z., et al. (2022). Multi-modal Contrastive Representation Learning for Entity Alignment. COLING, 2572–2584.

Sainiów, X. (2024). What to align in multimodal contrastive learning? OpenReview.

Frontiers in Emerging Artificial Intelligence and Machine Learning

Article Details Page