Evaluating zero-shot transfers and multilingual models for dependency parsing and POS tagging within the low-resource language family Tupían

Abstract

This work presents two experiments with the goal of replicating the transferability of dependency parsers and POS taggers trained on closely related languages within the low-resource language family Tupían. The experiments include both zero-shot settings as well as multilingual models. Previous studies have found that even a comparably small treebank from a closely related language will improve sequence labelling considerably in such cases. Results from both POS tagging and dependency parsing confirm previous evidence that the closer the phylogenetic relation between two languages, the better the predictions for sequence labelling tasks get. In many cases, the results are improved if multiple languages from the same family are combined. This suggests that in addition to leveraging similarity between two related languages, the incorporation of multiple languages of the same family might lead to better results in transfer learning for NLP applications.

Tasks

Reproductions