Multimodal Intermediate Tasks for Zero-Shot Cross-Lingual Transfer on XTREME-R

There has been a recent spike in interest in multi-modal Language and Vision problems. On the language side, most of these models primarily focus on English since most multi-modal datasets are monolingual. We try to bridge this gap with a zero-shot approach for learning multi-modal representations using cross-lingual pre-training on the text side. We present a simple yet practical approach for building a cross-lingual image retrieval model which trains on a monolingual training dataset but can be used in a zero-shot cross-lingual fashion during inference. We also introduce a new objective func

Research goal: Do multimodal intermediate tasks (e.g., image-text retrieval) improve zero-shot cross-lingual transfer performance on XTREME-R compared to English-only text-based intermediate tasks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.