Efficient Multilingual Multi-modal Pre-training through Triple Contrastive Loss
Youhan Lee, KyungTae Lim, Woonhyuk Baek, Byungseok Roh, Saehoon Kim
Abstract
Learning visual and textual representations in the shared space from web-scale image-text pairs improves the performance of diverse vision-and-language tasks, as well as modality-specific tasks. Many attempts in this framework have been made to connect English-only texts and images, and only a few works have been proposed to extend this framework in multilingual settings with the help of many translation pairs. In this multilingual approach, a typical setup is to use pairs of (image and English-text) and translation pairs. The major limitation of this approach is that the learning signal of aligning visual representation with under-resourced language representation is not strong, achieving a sub-optimal performance of vision-and-language tasks. In this work, we propose a simple yet effective enhancement scheme for previous multilingual multi-modal representation methods by using a limited number of pairs of images and non-English texts. In specific, our scheme fine-tunes a pre-trained multilingual model by minimizing a triplet contrastive loss on triplets of image and two different language texts with the same meaning, improving the connection between images and non-English texts. Experiments confirm that our enhancement strategy achieves performance gains in image-text retrieval, zero-shot image classification, and sentence embedding tasks.- Anthology ID:
- 2022.coling-1.504
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 5730–5744
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.504
- DOI:
- Cite (ACL):
- Youhan Lee, KyungTae Lim, Woonhyuk Baek, Byungseok Roh, and Saehoon Kim. 2022. Efficient Multilingual Multi-modal Pre-training through Triple Contrastive Loss. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5730–5744, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Efficient Multilingual Multi-modal Pre-training through Triple Contrastive Loss (Lee et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2022.coling-1.504.pdf
- Data
- CCMatrix, CIFAR-100, Flickr30k, Food-101, ImageNet, MS COCO