ivanvykopal / adapter-prompt-evaluation

Soft Language Prompts for Language Transfer

This is the source code for the paper Soft Language Prompts for Language Transfer.

Abstract

Cross-lingual knowledge transfer, especially between high- and low-resource languages, remains a challenge in natural language processing (NLP). This study offers insights for improving cross-lingual NLP applications through the combination of parameter-efficient fine-tuning methods. We systematically explore strategies for enhancing this cross-lingual transfer through the incorporation of language-specific and task-specific adapters and soft prompts. We present a detailed investigation of various combinations of these methods, exploring their efficiency across six languages, focusing on three low-resource languages, including the to our knowledge first use of soft language prompts. Our findings demonstrate that in contrast to claims of previous work, a combination of language and task adapters does not always work best; instead, combining a soft language prompt with a task adapter outperforms other configurations in many cases.

Reproducibility

Installation

To replicate our experiments, ensure you have Python version 3.11.8 and install the required packages listed in requirements.txt using the following command:

pip install -r requirements.txt

Reproduction of the results

We have prepared several scripts to facilitate the training of language and task-specific adapters and soft prompts. The following sections describe how to use these scripts.

Ensure that all scripts are executed from the src folder.

Language representations

To train language representations, run:

python -m scripts.lm

This script (scripts/lm.py) trains the language-specific adapters and soft prompts using the Wikipedia dump available on HuggingFace. The exact version of the dump used is specified within the script.

Task representations

We have multiple scripts to train task-specific adapters and soft prompts, as we evaluated various configurations of these methods.

Available scripts include:

• scripts/qa - for training task-specific adapters and soft prompts for the question answering task
• scripts/ner - for training task-specific adapters and soft prompts for the named-entity recognition task
• scripts/xnli - for training task-specific adapters and soft prompts for the natural language inference task
• scripts/checkworthy - for training task-specific adapters and soft prompts for check-worthy claim detection

For example, to run the script for question answering, use:

python -m scripts.qa

Cross-lingual evaluation

In our experiments, we assess cross-lingual performance by training task representations in one language and evaluating them in other languages. For this evaluation, use the script scripts/crosslingual.py.

To evaluate trained task representations on English data and test them on other languages, run:

python -m scripts.crosslingual --trained_language english --test_languages german spanish slovak czech telugu

Paper citing

If you use the code or information from this repository, please cite our paper, which will be available on arXiv.

@misc{vykopal2024prompteval,
      title={Soft Language Prompts for Language Transfer}, 
      author={Ivan Vykopal and Simon Ostermann and Marián Šimko},
      year={2024},
}