构建罗马尼亚总统问答数据集，研究政治回避行为跨语言迁移规律
PolERo: Studying Political Evasion in Romanian
- 基于3574对总统答问构建多层级回避标注数据集
- 跨语言迁移中模型表现不对称，语用线索类回避仍难识别
- 适合关注政治话语分析、跨语言NLP的学者和实践者
政治回避指回应问题却隐匿关键信息的现象。现有NLP研究将该现象视为分类任务，采用双层分类体系（回答清晰度与细粒度回避策略）。但当前研究仅限英文语境，尚未验证该体系在其他语言与政治背景下的可迁移性。本文提出PolERo数据集，包含从五位罗马尼亚总统官方发言记录中提取的3,574对人工标注的问答对。在相同条件下评估多种分类方法：TF-IDF基线、微调编码器模型、提出的滑动窗口编码器及零样本/少样本大模型提示。通过联合双语训练与机器翻译增强数据，研究跨语言迁移。结果表明：微调编码器表现优异，跨语言迁移存在不对称性，涉及语用线索的模糊回避类别仍是各类模型的主要挑战。
原文摘要 · Abstract (English)
Political evasion refers to responses that engage with a question while withholding the requested information. Recent NLP work frames political evasion as a classification task using a two-level taxonomy of response clarity and fine-grained evasion strategies. Existing work on response clarity and evasion classification is limited to English, leaving open whether the taxonomy and model behavior transfer across languages and political contexts. We introduce PolERo, a dataset of 3,574 human-annotated question-answer pairs extracted from official transcripts of five Romanian presidents. We evaluate multiple classification approaches on both datasets under matched conditions, including TF-IDF baselines, fine-tuned encoder models, a proposed sliding-window encoder, and zero/few-shot LLM prompting. We study cross-lingual transfer through joint bilingual training and machine-translation-based data augmentation. Our results indicate that fine-tuned encoders are competitive, cross-lingual transfer is asymmetric, and ambivalent evasion categories involving pragmatic cues remain the main challenge across all model families.
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