SG-UniBuc-NLP at SemEval-2026 Task 6: Multi-Head RoBERTa with Chunking for Long-Context Evasion Detection
In the rapidly evolving field of natural language processing (NLP), the ability to analyze political discourse is becoming increasingly significant. The SG-UniBuc-NLP team has made notable strides in this area by participating in the SemEval-2026 Task 6, known as CLARITY: Unmasking Political Question Evasions. This task focuses on classifying political interview responses based on their clarity and evasion strategies, presenting unique challenges due to the length and complexity of the text involved.
System Overview
The SG-UniBuc-NLP system employs a robust architecture that leverages the capabilities of the RoBERTa-large model. This choice was made to handle the intricacies of political language while maintaining high accuracy in classification tasks. The system is designed to classify responses into:
- Coarse-grained clarity: A three-way classification indicating the clarity of the response.
- Fine-grained evasion strategy: A nine-way classification that identifies specific evasion tactics employed by the respondents.
Chunking Strategy
One of the primary challenges faced during the implementation of the SG-UniBuc-NLP system is the standard 512-token limit imposed by Transformer encoders. To address this limitation, the team developed a novel overlapping sliding-window chunking strategy. This approach allows the model to process longer responses without losing critical contextual information. The chunking process is complemented by an element-wise Max-Pooling aggregation technique, which effectively combines the representations of each chunk to form a comprehensive understanding of the entire response.
Multi-Task Learning Approach
The architecture features a shared RoBERTa-large encoder that is fine-tuned for both subtasks through a multi-task learning framework. This design not only optimizes the performance of the model but also enhances its ability to generalize across different types of political discourse. During training, the model was subjected to a joint multi-task objective that encouraged cooperation between the two classification tasks.
Evaluation and Results
The performance of the SG-UniBuc-NLP system was rigorously evaluated using 7-fold stratified cross-validation during the inference stage. This method ensured that the model’s predictions were robust and reliable across various subsets of data. The results were impressive, with the system achieving a Macro-F1 score of:
- 0.80 on Subtask 1: This score reflects the model’s effectiveness in classifying the clarity of political responses.
- 0.51 on Subtask 2: Although lower, this score illustrates the model’s capability to identify specific evasion strategies.
Ultimately, the SG-UniBuc-NLP team secured the 11th position in both subtasks, showcasing their innovative approach and commitment to advancing NLP applications in political contexts.
Conclusion
The participation of SG-UniBuc-NLP in SemEval-2026 Task 6 highlights the potential of leveraging advanced transformer architectures, like RoBERTa, for analyzing complex political language. As the demand for more nuanced understanding of political discourse grows, methodologies such as those developed by SG-UniBuc-NLP will play a crucial role in enhancing the interpretability and analysis of political communications.
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