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。

跨语言匹配推文与适用的事实检查

Matching Tweets With Applicable Fact-Checks Across Languages

Multilingual transformer models like mBERT and XLM-RoBERTa have obtained
great improvements for many NLP tasks on a variety of languages. However,
recent works also showed that results from high-resource languages could not be
easily transferred to realistic, low-resource scenarios. In this work, we study
trends in performance for different amounts of available resources for the
three African languages Hausa, isiXhosa and Yor\`ub\'a on both NER and topic
classification. We show that in combination with transfer learning or distant
supervision, these models can achieve with as little as 10 or 100 labeled
sentences the same performance as baselines with much more supervised training
data. However, we also find settings where this does not hold. Our discussions
and additional experiments on assumptions such as time and hardware
restrictions highlight challenges and opportunities in low-resource learning.

研究了不同数量可用资源的情况下，对三种非洲语言 Hausa，isiXhosa 和 Yoruba 进行 NER 和主题分类的表现趋势，并通过远距离监督和迁移学习等方法，使用少量的标注数据达到与基线相同的性能，揭示了低资源学习中存在的挑战和机会。