arXiv:1907.04307cs.CL
Multilingual Universal Sentence Encoder for Semantic Retrieval
Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.
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.
This is an area that's dear to me, I'm the cofounder of Vectara and have been working with embedding-based semantic search, aka neural search or neural IR, since 2017. To whether Google uses semantic search, the answer is yes, very heavily [1][2]. Not only that, but they have…
svcrunchView on HNParent context