Combining Static and Contextualised Multilingual Embeddings
This work addresses the challenge of aligning multilingual embeddings for researchers and practitioners in NLP, offering an incremental improvement over existing methods.
The paper tackled the problem of improving multilingual representations by combining static and contextual embeddings, resulting in high-quality static embeddings for 40 languages and positive results on complex semantic tasks without requiring parallel text.
Static and contextual multilingual embeddings have complementary strengths. Static embeddings, while less expressive than contextual language models, can be more straightforwardly aligned across multiple languages. We combine the strengths of static and contextual models to improve multilingual representations. We extract static embeddings for 40 languages from XLM-R, validate those embeddings with cross-lingual word retrieval, and then align them using VecMap. This results in high-quality, highly multilingual static embeddings. Then we apply a novel continued pre-training approach to XLM-R, leveraging the high quality alignment of our static embeddings to better align the representation space of XLM-R. We show positive results for multiple complex semantic tasks. We release the static embeddings and the continued pre-training code. Unlike most previous work, our continued pre-training approach does not require parallel text.