The purpose of this project is to propose models for the shared-task task 0 problem. To do so, we will use the development data to design two models (a neural and a non neural) that given a lemma and a set of morphological attributes return the corresponding form.
The data used in this project are available here. These are developmental languages and surprise languages for the SIGMORPHON 2020 task 0 (Typologically Diverse Morphlogical Inflection). The specificity of this dataset lies in the fact that it contains several language families, each of which contains a different number of languages. Here is an example from swahli (swa) language.
| lemma | form | morphological attributes |
|---|---|---|
| piga | amepiga | V;PRF;FIN;IND;SG;3;PST |
| kamilisha | wangekamilisha | V;FIN;COND;PL;3;PRS |
| fuata | walifuata | V;FIN;IND;PL;3;PST |
| tengeneza | nitatengeneza | V;FIN;IND;SG;1;FUT |
| uwa | anauwa | V;DEF;FIN;IND;SG;3;PRS |
python bag_of_words.py train_file test_file verbose=1
python prefix_suffix.py train_file test_file verbose=1
python prefix_suffix_improved.py train_file test_file verbose=1
python root_prefix_suffix.py train_file test_file verbose=1
To try our implementation in your own machine, you need to download the data from task0-data and install the following requirements:
pip install -r requirements.txt
@article{vylomova2020sigmorphon, title={SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection}, author={Vylomova, Ekaterina and White, Jennifer and Salesky, Elizabeth and Mielke, Sabrina J and Wu, Shijie and Ponti, Edoardo and Maudslay, Rowan Hall and Zmigrod, Ran and Valvoda, Josef and Toldova, Svetlana and others}, journal={SIGMORPHON 2020}, pages={1}, year={2020}}