Russian neural morphological tagging: do not merge tagsets
A. A. Movsesyan · Computational Linguistics and Intellectual Technologies · 2022
There are multiple morphologically annotated corpora of Russian available. They have different tagsets and annotation guidelines, which makes them difficult to use together. We proposed a neural morphological tagger for Russian based on multitask learning technique which is able to predict morphological tags of words for different tagsets. We evaluated our model on various corpora and showed that utilising multiple corpora without merging them not only improves tagging performance but allows for scalable indirect conversion between multiple tagsets in all directions. Furthermore, we also showed that treating each corpus separately is more efficient than merging the corpora even if they share the same tagset.