Multilingual Universal Sentence Encoder for Semantic Retrieval

被引：0

作者：

Yang, Yinfei ^{[1
]}

Cer, Daniel ^{[1
]}

Ahmad, Amin ^{[1
]}

Guo, Mandy ^{[1
]}

Law, Jax ^{[1
]}

Constant, Noah ^{[1
]}

Abrego, Gustavo Hernandez ^{[1
]}

Yuan, Steve ^{[2
]}

Tar, Chris ^{[1
]}

Sung, Yun-Hsuan ^{[1
]}

Strope, Brian ^{[1
]}

Kurzweil, Ray ^{[1
]}

机构：

[1] Google AI, Mountain View, CA 94043 USA

[2] Google, Cambridge, MA USA

来源：

58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020): SYSTEM DEMONSTRATIONS | 2020年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present easy-to-use retrieval focused multilingual sentence embedding models, made available on TensorFlow Hub. The models embed text from 16 languages into a shared semantic space using a multi-task trained dual-encoder that learns tied cross-lingual representations via translation bridge tasks (Chidambaram et al., 2018). The models achieve a new state-of-the-art in performance on monolingual and cross-lingual semantic retrieval (SR). Competitive performance is obtained on the related tasks of translation pair bitext retrieval (BR) and retrieval question answering (ReQA). On transfer learning tasks, our multilingual embeddings approach, and in some cases exceed, the performance of English only sentence embeddings.

引用

页码：87 / 94

页数：8

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