M3P: Learning Universal Representations via Multitask Multilingual Multimodal Pre-training
This work addresses the challenge of aligning images with non-English text for multilingual multimodal tasks, representing an incremental advancement in pre-training methods.
The paper tackles the problem of learning universal representations across languages and modalities by introducing M3P, a multitask multilingual multimodal pre-trained model, which achieves comparable results for English and new state-of-the-art results for non-English languages on multilingual image retrieval benchmarks.
We present M3P, a Multitask Multilingual Multimodal Pre-trained model that combines multilingual pre-training and multimodal pre-training into a unified framework via multitask pre-training. Our goal is to learn universal representations that can map objects occurred in different modalities or texts expressed in different languages into a common semantic space. In addition, to explicitly encourage fine-grained alignment between images and non-English languages, we also propose Multimodal Code-switched Training (MCT) to combine monolingual pre-training and multimodal pre-training via a code-switch strategy. Experiments are performed on the multilingual image retrieval task across two benchmark datasets, including MSCOCO and Multi30K. M3P can achieve comparable results for English and new state-of-the-art results for non-English languages.