Part-of-Speech Tagging on an Endangered Language: a Parallel Griko-Italian Resource
Table 4: The best performing model is the one that combines semi-supervised learning with cross-lingual projected tags (G&B+clp). All models except for crf-mod benefit from transfer learning through alignments (+clp). Transduction does not significantly affect performance, except for the neural model.
['Model', 'Data  [ITALIC] no transduction', 'Data  [ITALIC] no transduction', 'Data  [ITALIC] transduction']
We find that the crf-mod model is the best baseline model. With such few data to train on, both the crf and the neural model do not perform well. The bi- and tri-gram features that the crf-mod model uses are very sparse, while the neural model has to deal with a very large number of unknown words, as discussed below in the Analysis subsection. The crf-mod method uses sparser features and is more prone to errors due to the noise of the projections. The best performance is achieved when we combine the projected tags, as type-level supervision, with the G&B method that leverages monolingual data. Their combination achieves the best overall performance, with an accuracy of 72.9%, a significant improvement over all other methods. As far as we know, this is the first time that cross-lingual projected tags are combined with the method of \citetgarrette2013learning.