This is an implementation of our paper: XFEVER: Exploring Fact Verification across Languages. If you find our code and/or models useful, please cite our paper:
@inproceedings{chang-etal-2023-xfever,
title = "{XFEVER}: Exploring Fact Verification across Languages",
author = "Chang, Yi-Chen and
Kruengkrai, Canasai and
Yamagishi, Junichi",
booktitle = "Proceedings of the 35th Conference on Computational Linguistics and Speech Processing (ROCLING 2023)",
url = "https://aclanthology.org/2023.rocling-1.1",
pages = "1--11",
year = "2023",
}
The code is tested on Python 3.9 and PyTorch 1.12.1. We recommend to create a new environment for experiments using conda:
conda create -y -n xfever python=3.9
conda activate xfever
Then, from the xfever
project root, run:
pip install -r requirements.txt
For further development or modification, we recommend installing pre-commit
:
pre-commit install
To ensure that PyTorch is installed and CUDA works properly, run:
python -c "import torch; print(torch.__version__); print(torch.cuda.is_available())"
We should see:
1.12.1+cu113
True
See experiments.
This work is supported by JST CREST Grants (JPMJCR18A6 and JPMJCR20D3) and MEXT KAKENHI Grants (21H04906), Japan.
BSD 3-Clause License
Copyright (c) 2023, Yamagishi Laboratory, National Institute of Informatics All rights reserved.
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