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20162019
most citedMultilingual Universal Sentence Encoder for Semantic Retrieval

67 citations · 81 across the 3 of their papers we have counts for

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Showing cs.CLShow all

8 papers · 1 filter

cs.CL201967 cited

Multilingual Universal Sentence Encoder for Semantic Retrieval

Yinfei Yang, Daniel Cer, Amin Ahmad +9

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…

cs.CL20192 cited

Hierarchical Document Encoder for Parallel Corpus Mining

Mandy Guo, Yinfei Yang, Keith Stevens +5

We explore using multilingual document embeddings for nearest neighbor mining of parallel data. Three document-level representations are investigated: (i) document embeddings gener…

cs.CL201912 cited

Improving Multilingual Sentence Embedding using Bi-directional Dual Encoder with Additive Margin Softmax

Yinfei Yang, Gustavo Hernandez Abrego, Steve Yuan +6

In this paper, we present an approach to learn multilingual sentence embeddings using a bi-directional dual-encoder with additive margin softmax. The embeddings are able to achieve…

cs.CL2018

Learning Cross-Lingual Sentence Representations via a Multi-task Dual-Encoder Model

Muthuraman Chidambaram, Yinfei Yang, Daniel Cer +4

A significant roadblock in multilingual neural language modeling is the lack of labeled non-English data. One potential method for overcoming this issue is learning cross-lingual t…

cs.CL2018

Effective Parallel Corpus Mining using Bilingual Sentence Embeddings

Mandy Guo, Qinlan Shen, Yinfei Yang +8

This paper presents an effective approach for parallel corpus mining using bilingual sentence embeddings. Our embedding models are trained to produce similar representations exclus…

cs.CL2018

Learning Semantic Textual Similarity from Conversations

Yinfei Yang, Steve Yuan, Daniel Cer +7

We present a novel approach to learn representations for sentence-level semantic similarity using conversational data. Our method trains an unsupervised model to predict conversati…