用分块策略处理超长政治回答，提升避答检测准确率。
SG-UniBuc-NLP at SemEval-2026 Task 6: Multi-Head RoBERTa with Chunking for Long-Context Evasion Detection
- 分块滑动窗口+最大池化，突破Transformer长度限制
- 多任务联合训练，细粒度避答策略识别准确率达51%
- 适合关注长文本分析与政治话语研究的读者
我们描述了参与SemEval-2026任务6（CLARITY：揭露政治问题避答）的系统，该任务对英语政治访谈回答进行粗粒度清晰度（三分类）和细粒度避答策略（九分类）分类。由于回答常超过标准Transformer编码器的512词元限制，我们采用重叠滑动窗口分块策略，并对各块表示进行逐元素最大池化聚合。共享的RoBERTa-large编码器通过多任务目标联合训练两个特定任务头，推理时结合7折分层交叉验证的集成结果。系统在子任务1上取得0.80的宏平均F1，在子任务2上取得0.51，两个子任务均排名第11。
原文摘要 · Abstract (English)
We describe our system for SemEval-2026 Task 6 (CLARITY: Unmasking Political Question Evasions), which classifies English political interview responses by coarse-grained clarity (3-way) and fine-grained evasion strategy (9-way). Since responses frequently exceed the 512-token limit of standard Transformer encoders, we apply an overlapping sliding-window chunking strategy with element-wise Max-Pooling aggregation over chunk representations. A shared RoBERTa-large encoder supplies two task-specific heads trained jointly via a multi-task objective, with inference-time ensembling over 7-fold stratified cross-validation. Our system achieves a Macro-F1 of 0.80 on Subtask 1 and 0.51 on Subtask 2, ranking 11th in both subtasks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。