  An important challenge for news fact-checking is the effective dissemination
of existing fact-checks. This in turn brings the need for reliable methods to
detect previously fact-checked claims. In this paper, we focus on automatically
finding existing fact-checks for claims made in social media posts (tweets). We
conduct both classification and retrieval experiments, in monolingual (English
only), multilingual (Spanish, Portuguese), and cross-lingual (Hindi-English)
settings using multilingual transformer models such as XLM-RoBERTa and
multilingual embeddings such as LaBSE and SBERT. We present promising results
for "match" classification (86% average accuracy) in four language pairs. We
also find that a BM25 baseline outperforms or is on par with state-of-the-art
multilingual embedding models for the retrieval task during our monolingual
experiments. We highlight and discuss NLP challenges while addressing this
problem in different languages, and we introduce a novel curated dataset of
fact-checks and corresponding tweets for future research.
