  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.
