README.md

This is the code for training target language-ready (TLR) task adapters with torch and adapter-transformers. Please refer to our paper Cross Lingual Transfer with Target Language-Ready Task Adapters for background.

Installation

First, install python >= 3.9 and pytorch >= 1.12.1, e.g. using conda:

conda create -n tlr-env python=3.9
conda activate tlr-env
conda install pytorch==1.12.1 pytorch-cuda=11.6 -c pytorch -c nvidia

Then download and install tlr-adapters:

git clone https://github.com/parovicm/tlr-adapters
cd tlr-adapters
pip install -e .

Training TLR adapters

The code for training TLR adapters is given by the TLR trainer which modifies the original Trainer from the adapter-transformers.

Scripts for all tasks supported are provided in examples/.

For example, the script for training the TASK-MULTI variant (see the paper for the explanation) for the AmericasNLI dataset is given here. Generally, unless you are using a single language adapter during task adapter training (this is the case with MAD-X and TARGET variants) you need to pass the file containing language adapters to be used. For the TASK_MULTI variant of the AmericasNLI dataset language adapters file will have the following content:

en PATH_TO_EN_LANG_ADAPTER
aym PATH_TO_AYM_LANG_ADAPTER
bzd PATH_TO_BZD_LANG_ADAPTER
cni PATH_TO_CNI_LANG_ADAPTER
gn PATH_TO_GN_LANG_ADAPTER
hch PATH_TO_HCH_LANG_ADAPTER
nah PATH_TO_NAH_LANG_ADAPTER
oto PATH_TO_OTO_LANG_ADAPTER
quy PATH_TO_QUY_LANG_ADAPTER
tar PATH_TO_TAR_LANG_ADAPTER
shp PATH_TO_SHP_LANG_ADAPTER

Citation

If you use this code, please cite the following paper:

Marinela Parović, Alan Ansell, Ivan Vulić, and Anna Korhonen. 2023. Cross Lingual Transfer with Target Language-Ready Task Adapters. In *Findings of the 61st Annual Meeting of the Association for Computational Linguistics.