Combating the Curse of Multilinguality in Cross-Lingual WSD by Aligning
  Sparse Contextualized Word Representations
Gábor Berend
In this paper, we advocate for using large pre-trained monolingual language
models in cross lingual zero-shot word sense disambiguation (WSD) coupled with
a contextualized mapping mechanism. We also report rigorous experiments that
illustrate the effectiveness of employing sparse contextualized word
representations obtained via a dictionary learning procedure. Our experimental
results demonstrate that the above modifications yield a significant
improvement of nearly 6.5 points of increase in the average F-score (from 62.0
to 68.5) over a collection of 17 typologically diverse set of target languages.
We release our source code for replicating our experiments at
https://github.com/begab/sparsity_makes_sense.