多语言目标立场抽取
Multilingual Target-Stance Extraction
AI总结：
该研究首次构建涵盖六种语言的多语言目标立场抽取基准，扩展原有流程无需分语言建模，虽F1仅12.78，但揭示了目标预测瓶颈及表述敏感性，为后续研究提供基线。
摘要：
社交媒体使得对争议问题的公众舆论进行数据驱动分析成为可能。目标立场抽取（TSE）是识别文档中讨论的目标以及文档对该目标所持立场的任务。许多研究在多语言环境下对给定目标进行立场分类，但所有先前的TSE工作都仅限英语。本研究引入了首个多语言TSE基准，涵盖加泰罗尼亚语、爱沙尼亚语、法语、意大利语、普通话和西班牙语语料库。该方法成功地将原始TSE流程扩展到多语言环境，无需为每种语言单独训练模型。我们的模型流程取得了12.78的适度F1分数，这凸显了多语言任务相对于仅英语设置的难度增加，并指出目标预测是主要瓶颈。我们也是首个证明TSE的F1分数对不同目标表述具有敏感性的研究。这些共同为多语言TSE中的资源、算法和评估标准提供了急需的基线。
英文摘要：
Social media enables data-driven analysis of public opinion on contested issues. Target-Stance Extraction (TSE) is the task of identifying the target discussed in a document and the document's stance towards that target. Many works classify stance towards a given target in a multilingual setting, but all prior work in TSE is English-only. This work introduces the first multilingual TSE benchmark, spanning Catalan, Estonian, French, Italian, Mandarin, and Spanish corpora. It manages to extend the original TSE pipeline to a multilingual setting without requiring separate models for each language. Our model pipeline achieves a modest F1 score of 12.78, underscoring the increased difficulty of the multilingual task relative to English-only setups and highlighting target prediction as the primary bottleneck. We are also the first to demonstrate the sensitivity of TSE's F1 score to different target verbalizations. Together these serve as a much-needed baseline for resources, algorithms, and evaluation criteria in multilingual TSE.
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。
