## 1 Introduction  
Misinformation is low-cost, wide-spreading, and large-scale replicable on social media, which has significantly influenced democracy, justice, and public trust [1]. Especially with the support of new multimedia technology, such as short videos on social media, misinformation spread has transcended countries and languages. Research indicates that the proportion of false claims is only approximately 1% of total news dissemination on all platforms, while the percentage of false claims is almost more than 6% of total tweets on social media [2]. Human fact-checking is of high-quality but is time-consuming, it refers to specifically confronting false information with a deceptive purpose, such as offensive news, political deception, and dramatic online rumors around the world. The absence of professional auditors, particularly who understand minority languages, further increases the challenge of multilingual fact-checking. Thus, multilingual automated fact-checking has become urgent to research, including the release of multilingual fact-checking datasets and inventing computational approaches. Multilingual fact-checking is a special fact-checking task that aims to leverage related evidence from a multilingual text corpus to verify a multilingual textual claim. Figure 1 for example, shows two such samples from Portuguese and German. In the special task, how to effectively retrieve, align, integrate, infer, and verify is the major challenge and still remains an open question, especially leveraging resources from different languages in multilingual scenes. In previous studies, a possible approach for multilingual verification was to utilize translation systems (e.g., Google Translator) for news verification based on multilingual evidence, which hypothesized that fake news tended to be less widespread across languages as compared to authoritative news [3]. However, the threshold value of the modeling pipeline needed to be set manually. In consequence, the end-to-end frameworks based on multilingual fine-tuning transformers were designed to encode multilingual claims and evidence for fact-checking. [PERSON], [PERSON] et al. [4] proposed the EMET framework based on a convolutional neural network to classify the reliability of messages posted on social media platforms. [PERSON] and [PERSON] [5] built a BERT classifier to detect the false class and other fact-check articles. [PERSON] and [PERSON] [6] next proposed an automated multi-modal content approach based on the multilingual fact verification system, which automated the task of fact-verifying websites, and provided evidence for each judgment. Nevertheless, they merely utilized simple evidence combination methods such as concatenating the evidence or just dealing with each evidence-claim pair. Going further, in addition to considering the use of pre-trained models to achieve multilingual semantics, another promising direction is to distill knowledge from high-resource to low-resource languages. [PERSON], [PERSON] et al. [3] adopted the teacher-student framework to train a multilingual embedding model based on the XLM-R model, in which a high-quality teacher model is adopted to promote the learning ability of a student model. With the expansion of language categories, a potentially troublesome problem in fine-grained multilingual fact-checking in this way is that the teacher model has maybe never seen the statements expressed in a minority language. Thus, the intensive research of few-shot or zero-shot multilingual fact-checking has been subject to considerable discussion. [PERSON], [PERSON] et al. [7] proposed FaceTeR-Check architecture for semi-automated fact-checking, which enables several modules to evaluate semantic similarity to calculate natural language inference and to retrieve information from online social networks. The multilingual fact  
Figure 1: Typical Examples from X-FACT. Example #1 and #2 are from Portuguese and Spanish, respectively. Every sample consists of a claim, evidence, and label.