Papers by Xiaocong Du

2 papers
Harmless Transfer Learning for Item Embeddings (2022.findings-naacl)

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Challenge: Existing approaches to learn item embeddings for categorical features are limited by the frequency of items in real-world.
Approach: They propose a method that transfers knowledge from frequent items to rare items by introducing an auxiliary transfer loss.
Outcome: The proposed framework significantly boosts the performance on a variety of NLP and recommendation system tasks.
Analyze Like a Venture Capitalist: Information-Gain and Knowledge Enhanced Graph Reasoning for Startup Success Prediction (2026.findings-acl)

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Challenge: Most venture capital investments fail, while a few deliver outsized returns.
Approach: They propose a framework that synthesizes relational evidence across sources . they propose combining information-gain-driven retriever and knowledge base to ground reasoning .
Outcome: The proposed framework achieves +5.9% F1 and +22.1% Precision@5 over state-of-the-art baselines.

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