Multilingual Factor Analysis

ACL 2019

Multilingual Factor Analysis

Jan 30, 2021
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Abstract: In this work we approach the task of learning multilingual word representations in an offline manner by fitting a generative latent variable model to a multilingual dictionary. We model equivalent words in different languages as different views of the same word generated by a common latent variable representing their latent lexical meaning. We explore the task of alignment by querying the fitted model for multilingual embeddings achieving competitive results across a variety of tasks. The proposed model is robust to noise in the embedding space making it a suitable method for distributed representations learned from noisy corpora. Authors: Francisco Vargas, Kamen Brestnichki, Alex Papadopoulos Korfiatis, Nils Hammerla (Babylon Health)

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