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arXiv · 1907.04307

Multilingual Universal Sentence Encoder for Semantic Retrieval

Yinfei Yang·Daniel Cer·Amin Ahmad·Mandy Guo·Jax Law·Noah Constant·Gustavo Hernandez Abrego·Steve Yuan·Chris Tar·Yun-Hsuan Sung·Brian Strope·Ray Kurzweil
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Abstract

We introduce two pre-trained retrieval focused multilingual sentence encoding models, respectively based on the Transformer and CNN model architectures. The models embed text from 16 languages into a single semantic space using a multi-task trained dual-encoder that learns tied representations using translation based bridge tasks (Chidambaram al., 2018). The models provide performance that is competitive with the state-of-the-art on: semantic retrieval (SR), translation pair bitext retrieval (BR) and retrieval question answering (ReQA). On English transfer learning tasks, our sentence-level embeddings approach, and in some cases exceed, the performance of monolingual, English only, sentence embedding models. Our models are made available for download on TensorFlow Hub.

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Yinfei Yang, Daniel Cer, Amin Ahmad, Mandy Guo, Jax Law, Noah Constant, Gustavo Hernandez Abrego, Steve Yuan, Chris Tar, Yun-Hsuan Sung, Brian Strope, Ray Kurzweil. 2019-07-09. Multilingual Universal Sentence Encoder for Semantic Retrieval. https://arxiv.org/abs/1907.04307

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