Papers by Zhongqiu Li

2 papers
Unregulated Chinese-to-English Data Expansion Does NOT Work for Neural Event Detection (2022.coling-1)

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Challenge: Experimental results show that cross-language data expansion results in performance degradation.
Approach: They leverage cross-language data expansion and retraining to enhance neural Event Detection on English ACE corpus.
Outcome: The proposed method improves ED performance by 1.6% over the straight data combination.
UCS-SQL: Uniting Content and Structure for Enhanced Semantic Bridging In Text-to-SQL (2025.findings-acl)

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Challenge: Existing methods overlook the challenge of effectively transforming structure information from NL to SQL.
Approach: They propose a text-to-SQL framework that unites content and structure pipes to bridge the gap between NL and SQL.
Outcome: The proposed framework bridges the gap between natural language questions and SQL by combining content and structure pipes.

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