This repository contains the implementation for our paper.

Efficient Test Time Adapter Ensembling for Low-resource Language Varieties
Xinyi Wang, Yulia Tsvetkov,Sebastian Ruder, Graham Neubig
EMNLP 2021 Findings

Our code is based on the adapter-transformers codebase and the XTREME benchmark

We find that specialized language adapters might not be robust to unseen language variations, and that utilization of multiple existing pretrained language adapters alleviates this issue. We propose an algorithm named EMEA(Entropy Minimized Ensemble of Language Adapters), which optimizes the ensemble weights of a group of related language adapters at test time for each test input.

The main function for optimizing the adapter weighting using EMEA is here.

We simply use the data downloading instruction from the official XTREME repo. We also provide the processed data for NER in data/.

To install the dependencies: pip install --editable .

EMEA is a test time decoding algorithm. You need to train a task adapter before testing out the different decoding strategies. Here we provide a pretrained NER task adapter in outputs/ner/.

Baseline bash job_scripts/test_panx_adapter.sh

Ensemble bash job_scripts/test_panx_adapter_ensemble.sh

EMEA-s1 bash job_scripts/test_panx_adapter_emea_s1.sh

EMEA-s10 bash job_scripts/test_panx_adapter_emea_s10.sh

Please cite our paper as:

@inproceedings{wang2021emea,
    title={Efficient Test Time Adapter Ensembling for Low-resource Language Varieties},
    author={Wang, Xinyi and
            Tsvetkov, Yulia and
            Ruder, Sebastian and
            Neubig, Graham},
    booktitle={EMNLP: Findings},
    year={2021}
}