This project was done as a part of UW-Madison's course CS839: Learn more with less supervision by Prof. Fred Sala.
Co-Author: Julia Kroll
We study Few-Shot POS-Tagging using prototype learning and self-training methods following huang et al.,'s work on NER, a similar sequence labeling task. The code was adapted from their codebase. We implement the two methods on three datasets: (i) CoNLL2000 (ii) UDPOS-English Web Treebank (iii) TED.
To run the code:
pip install -r requirements.txt
cd src
bash train_lc.sh
bash train_proto.sh
The results obtained were as follows:
Dataset | Model | Accuracy | shot |
---|---|---|---|
CoNLL | Self-Training | 0.9898 | 20-shot |
CoNLL | Prototype Learning | 0.9848 | 5-shot |
UDPOS | Self-Training | 0.8166 | 20-shot |
UDPOS | Prototype Learning | 0.8790 | 5-shot |
TED | Self-Training | 0.9692 | 20-shot |
TED | Prototype Learning | 0.9610 | 5-shot |
More details can be found in the report.