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.

本研究使用多语言变换器模型和嵌入技术（如 XLM-RoBERTa、LaBSE 和 SBERT 等）自动寻找社交媒体帖子（推文）中已经被事实检查过的主张。在不同语言设置中，我们进行了分类和检索实验，并取得了令人满意的结果。我们也发现处理不同语言时存在一些 NLP 挑战，并为未来的研究提供了一个新的事实检查和相应推文的数据集。