• Computer Science
  • Published in ArXiv 2019

Aligning Vector-spaces with Noisy Supervised Lexicons

@article{Lubin2019AligningVW,
  title={Aligning Vector-spaces with Noisy Supervised Lexicons},
  author={Noa Yehezkel Lubin and Jacob Goldberger and Yoav Goldberg},
  journal={ArXiv},
  year={2019},
  volume={abs/1903.10238}
}
The problem of learning to translate between two vector spaces given a set of aligned points arises in several application areas of NLP. [...] Key Method We propose a model that accounts for noisy pairs. This is achieved by introducing a generative model with a compatible iterative EM algorithm. The algorithm jointly learns the noise level in the lexicon, finds the set of noisy pairs, and learns the mapping between the spaces. We demonstrate the effectiveness of our proposed algorithm on two alignment problems…Expand Abstract

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Unsupervised Neural Machine Translation

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