XFEVER: Exploring Fact Verification across Languages

This paper introduces the Cross-lingual Fact Extraction and VERification (XFEVER) dataset designed for benchmarking the fact verification models across different languages. We constructed it by translating the claim and evidence texts of the Fact Extraction and VERification (FEVER) dataset into six languages. The training and development sets were translated using machine translation, whereas the test set includes texts translated by professional translators and machine-translated texts. Using the XFEVER dataset, two cross-lingual fact verification scenarios, zero-shot learning and translate-train learning, are defined, and baseline models for each scenario are also proposed in this paper. Experimental results show that the multilingual language model can be used to build fact verification models in different languages efficiently. However, the performance varies by language and is somewhat inferior to the English case. We also found that we can effectively mitigate model miscalibration by considering the prediction similarity between the English and target languages. The XFEVER dataset, code, and model checkpoints are available at https://github.com/nii-yamagishilab/xfever.

Learning to Paraphrase Sentences to Different Complexity Levels

While sentence simplification is an active research topic in NLP, its adjacent tasks of sentence complexification and same-level paraphrasing are not. To train models on all three tasks, we present two new unsupervised datasets. We compare these datasets, one labeled by a weak classifier and the other by a rule-based approach, with a single supervised dataset. Using these three datasets for training, we perform extensive experiments on both multitasking and prompting strategies. Compared to other systems trained on unsupervised parallel data, models trained on our weak classifier labeled dataset achieve state-of-the-art performance on the ASSET simplification benchmark. Our models also outperform previous work on sentence level targeting. Finally, we establish how a handful of Large Language Models perform on these tasks under a zero-shot setting.