Preprocessing/multilinguality/README.md

Zero-Shot Approximation of Language Embeddings

This directory contains all scripts that are needed to reproduce the meta learning for zero-shot part of our system. These scripts allow you to predict representations of languages purely based on distances between them, as measured by a variety of linguistically informed metrics, or even better, a learned combination thereof.

Learned distance metric

If you want to use a learned distance metric, you need to run MetricMetaLearner.py first to generate a lookup file for the learned distances.

Note: The learned distances are (obviously) only useful for the model it was trained on, i.e., different Toucan models require different learned-distance lookups.

Applying zero-shot approximation to a trained model

Use run_zero_shot_lang_emb_injection.py to update the language embeddings of a trained model for all languages that were not seen during training (by default, supervised_languages.json is used to determine which languages were seen). See the script for arguments that can be passed (e.g. to use a custom model path). Here is an example:

python run_zero_shot_lang_emb_injection.py -m <model_path> -d <distance_type> -k <number_of_nearest_neighbors>

By default, the updated model is saved with a modified filename in the same directory.