Papers by Xinmeng Hou

2 papers
Learn Like Humans: Use Meta-cognitive Reflection for Efficient Self-Improvement (2026.acl-long)

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Challenge: Existing self-improving frameworks rely on inefficient, multi-turn recursive loops that incur high computational costs.
Approach: They propose a framework that achieves efficient self-evolution within a single recurrence cycle.
Outcome: The proposed framework outperforms state-of-the-art self-evolving systems while significantly reducing computational overhead.
Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction (2025.findings-emnlp)

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Challenge: Existing approaches to extract aspects and opinions independently, optionally adding pairwise relations, often lead to error propagation and high time complexity.
Approach: They propose a transition-based model that performs aspect and opinion extraction jointly and integrates contrastive-augmented optimization.
Outcome: The proposed model outperforms previous models on two out of four datasets when trained on a single dataset.

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