Papers by Huaming Liao

2 papers
QUITO-X: A New Perspective on Context Compression from the Information Bottleneck Theory (2025.findings-emnlp)

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Challenge: Existing methods for compressing context by removing redundant tokens are inconsistent with the objective of retaining the most important tokens when conditioning on a given query.
Approach: They propose a method that uses information bottleneck theory to compress context . they propose to remove redundant tokens using metrics such as self-information or perplexity .
Outcome: The proposed method achieves a 25% increase in compression rate compared to the state-of-the-art .
CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction (2022.coling-1)

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Challenge: Existing solutions for quotation extraction use rule-based approaches and sequence labeling models.
Approach: They propose a Context and Former-Label Enhanced Net for quotation extraction.
Outcome: The proposed method achieves state-of-the-art performance on complicated quotation extraction on two public datasets and one proprietary dataset.

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