Krishna Srinivasan


2022

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Proceedings of the Workshop on Multilingual Multimodal Learning
Emanuele Bugliarello | Kai-Wei Cheng | Desmond Elliott | Spandana Gella | Aishwarya Kamath | Liunian Harold Li | Fangyu Liu | Jonas Pfeiffer | Edoardo Maria Ponti | Krishna Srinivasan | Ivan Vulić | Yinfei Yang | Da Yin
Proceedings of the Workshop on Multilingual Multimodal Learning

2021

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MURAL: Multimodal, Multitask Representations Across Languages
Aashi Jain | Mandy Guo | Krishna Srinivasan | Ting Chen | Sneha Kudugunta | Chao Jia | Yinfei Yang | Jason Baldridge
Findings of the Association for Computational Linguistics: EMNLP 2021

Both image-caption pairs and translation pairs provide the means to learn deep representations of and connections between languages. We use both types of pairs in MURAL (MUltimodal, MUltitask Representations Across Languages), a dual encoder that solves two tasks: 1) image-text matching and 2) translation pair matching. By incorporating billions of translation pairs, MURAL extends ALIGN (Jia et al.)–a state-of-the-art dual encoder learned from 1.8 billion noisy image-text pairs. When using the same encoders, MURAL’s performance matches or exceeds ALIGN’s cross-modal retrieval performance on well-resourced languages across several datasets. More importantly, it considerably improves performance on under-resourced languages, showing that text-text learning can overcome a paucity of image-caption examples for these languages. On the Wikipedia Image-Text dataset, for example, MURAL-base improves zero-shot mean recall by 8.1% on average for eight under-resourced languages and by 6.8% on average when fine-tuning. We additionally show that MURAL’s text representations cluster not only with respect to genealogical connections but also based on areal linguistics, such as the Balkan Sprachbund.