CrossNews-UA：乌克兰语、波兰语、俄语和英语的跨语言新闻语义相似性基准
CrossNews-UA: A Cross-lingual News Semantic Similarity Benchmark for Ukrainian, Polish, Russian, and English
AI总结：
针对跨语言新闻分析数据集可扩展性不足的问题，提出可解释众包流程构建CrossNews-UA基准，涵盖乌克兰语等四种语言，并评估多种模型，揭示多语言新闻分析挑战。
摘要：
在社交网络和虚假信息快速传播的时代，新闻分析仍然是一项关键任务。检测多种语言（尤其是英语以外的语言）的假新闻带来了重大挑战。跨语言新闻比较提供了一种有前景的方法，通过利用不同语言的外部来源来验证信息（Chen和Shu，2024）。然而，现有的跨语言新闻分析数据集（Chen等人，2022a）是由记者和专家手动整理的，限制了其可扩展性和对新语言的适应性。在这项工作中，我们通过引入一种可扩展、可解释的众包流程来解决这一差距，用于跨语言新闻相似性评估。利用该流程，我们收集了一个新颖的数据集CrossNews-UA，其中包含以乌克兰语为中心语言、并与语言和上下文相关的语言（波兰语、俄语和英语）的新闻对。每个新闻对都根据4W标准（谁、什么、哪里、何时）进行语义相似性标注，并附有详细的理由。我们进一步测试了一系列模型，从传统的词袋模型、基于Transformer的架构到大型语言模型（LLM）。我们的结果凸显了多语言新闻分析中的挑战，并为模型性能提供了见解。
英文摘要：
In the era of social networks and rapid misinformation spread, news analysis remains a critical task. Detecting fake news across multiple languages, particularly beyond English, poses significant challenges. Cross-lingual news comparison offers a promising approach to verify information by leveraging external sources in different languages (Chen and Shu, 2024). However, existing datasets for cross-lingual news analysis (Chen et al., 2022a) were manually curated by journalists and experts, limiting their scalability and adaptability to new languages. In this work, we address this gap by introducing a scalable, explainable crowdsourcing pipeline for cross-lingual news similarity assessment. Using this pipeline, we collected a novel dataset CrossNews-UA of news pairs in Ukrainian as a central language with linguistically and contextually relevant languages-Polish, Russian, and English. Each news pair is annotated for semantic similarity with detailed justifications based on the 4W criteria (Who, What, Where, When). We further tested a range of models, from traditional bag-of-words, Transformer-based architectures to large language models (LLMs). Our results highlight the challenges in multilingual news analysis and offer insights into models performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
