Burns, AndreaKim, DonghyunWijaya, DerrySaenko, KatePlummer, Bryan A.2021-12-202021-12-202020A. Burns, D. Kim, D. Wijaya, K. Saenko, B.A. Plummer. 2020. "Learning to Scale Multilingual Representations for Vision-Language Tasks.." CoRR, Volume abs/2004.04312, https://arxiv.org/abs/2004.04312https://hdl.handle.net/2144/43499Current multilingual vision-language models either require a large number of additional parameters for each supported language, or suffer performance degradation as languages are added. In this paper, we propose a Scalable Multilingual Aligned Language Representation (SMALR) that supports many languages with few model parameters without sacrificing downstream task performance. SMALR learns a fixed size language-agnostic representation for most words in a multilingual vocabulary, keeping language-specific features for just a few. We use a masked cross-language modeling loss to align features with context from other languages. Additionally, we propose a cross-lingual consistency module that ensures predictions made for a query and its machine translation are comparable. The effectiveness of SMALR is demonstrated with ten diverse languages, over twice the number supported in vision-language tasks to date. We evaluate on multilingual image-sentence retrieval and outperform prior work by 3-4% with less than 1/5th the training parameters compared to other word embedding methods.en-USComputer vision and pattern recognitionComputation and languageScalable Multilingual Aligned Language RepresentationArticle554253