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arXiv · 1905.12260

Learning Multilingual Word Embeddings Using Image-Text Data

Karan Singhal·Karthik Raman·Balder ten Cate
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Abstract

There has been significant interest recently in learning multilingual word embeddings -- in which semantically similar words across languages have similar embeddings. State-of-the-art approaches have relied on expensive labeled data, which is unavailable for low-resource languages, or have involved post-hoc unification of monolingual embeddings. In the present paper, we investigate the efficacy of multilingual embeddings learned from weakly-supervised image-text data. In particular, we propose methods for learning multilingual embeddings using image-text data, by enforcing similarity between the representations of the image and that of the text. Our experiments reveal that even without using any expensive labeled data, a bag-of-words-based embedding model trained on image-text data achieves performance comparable to the state-of-the-art on crosslingual semantic similarity tasks.

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BibTeXRIS

Karan Singhal, Karthik Raman, Balder ten Cate. 2019-05-29. Learning Multilingual Word Embeddings Using Image-Text Data. https://doi.org/10.18653/v1%2Fw19-1807

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