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Learning to scale multilingual representations for vision-language tasks
Burns, Andrea
;
Kim, Donghyun
;
Wijaya, Derry
;
Saenko, Kate
;
Plummer, Bryan A.
. - : Springer, 2020
Abstract:
Current 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. ; http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490188.pdf ; Published version
Keyword:
Image-sentence retrieval
;
Multilingual word embeddings
;
Scalable vision-language models
URL:
https://doi.org/10.1007/978-3-030-58548-8
https://hdl.handle.net/2144/42930
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