Aligning Vector-spaces with Noisy Supervised Lexicons

  title={Aligning Vector-spaces with Noisy Supervised Lexicons},
  author={Noa Yehezkel Lubin and Jacob Goldberger and Yoav Goldberg},
The problem of learning to translate between two vector spaces given a set of aligned points arises in several application areas of NLP. Current solutions assume that the lexicon which defines the alignment pairs is noise-free. We consider the case where the set of aligned points is allowed to contain an amount of noise, in the form of incorrect lexicon pairs and show that this arises in practice by analyzing the edited dictionaries after the cleaning process. We demonstrate that such noise… CONTINUE READING

Similar Papers

Loading similar papers…