SG-UniBuc-NLP at SemEval-2026 Task 6: Multi-Head RoBERTa with Chunking for Long-Context Evasion Detection
📰 ArXiv cs.AI
Learn to detect evasion in political interviews using Multi-Head RoBERTa with chunking for long-context analysis
Action Steps
- Apply overlapping sliding-window chunking to long texts to overcome token limits
- Use Multi-Head RoBERTa as the base model for text classification
- Implement element-wise Max-Pooling aggregation over chunk representations
- Fine-tune the model on a dataset of labeled political interview responses
- Evaluate the model's performance on coarse-grained clarity and fine-grained evasion strategy classification
Who Needs to Know This
NLP researchers and engineers can benefit from this approach to improve their text classification models, especially for tasks involving long-context analysis
Key Insight
💡 Chunking with Max-Pooling aggregation can effectively handle long-context texts in NLP tasks
Share This
Detect evasion in political interviews with Multi-Head RoBERTa & chunking! #NLP #SemEval
Key Takeaways
Learn to detect evasion in political interviews using Multi-Head RoBERTa with chunking for long-context analysis
Full Article
Title: SG-UniBuc-NLP at SemEval-2026 Task 6: Multi-Head RoBERTa with Chunking for Long-Context Evasion Detection
Abstract:
arXiv:2604.26375v1 Announce Type: cross Abstract: 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 shar
Abstract:
arXiv:2604.26375v1 Announce Type: cross Abstract: 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 shar
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