Manual fact-checking does not scale well to serve the needs of the internet.
This issue is further compounded in non-English contexts. In this paper, we
discuss claim matching as a possible solution to scale fact-checking. We define
claim matching as the task of identifying pairs of textual messages containing
claims that can be served with one fact-check. We construct a novel dataset of
WhatsApp tipline and public group messages alongside fact-checked claims that
are first annotated for containing "claim-like statements" and then matched
with potentially similar items and annotated for claim matching. Our dataset
contains content in high-resource (English, Hindi) and lower-resource (Bengali,
Malayalam, Tamil) languages. We train our own embedding model using knowledge
distillation and a high-quality "teacher" model in order to address the
imbalance in embedding quality between the low- and high-resource languages in
our dataset. We provide evaluations on the performance of our solution and
compare with baselines and existing state-of-the-art multilingual embedding
models, namely LASER and LaBSE. We demonstrate that our performance exceeds
LASER and LaBSE in all settings. We release our annotated datasets, codebooks,
and trained embedding model to allow for further research.

该研究讨论了声明匹配作为扩大事实核查的可能解决方案，通过使用知识蒸馏和高质量的教师模型训练自己的嵌入模型来改善低资源语言在数据集中嵌入质量的不平衡，并与现有的多语言嵌入模型进行比较，证明在所有设置中超过了现有的解决方案，并释放注释数据集和嵌入模型以供进一步研究。