Papers by Chenyan Lu
Less is More: Pretrain a Strong Siamese Encoder for Dense Text Retrieval Using a Weak Decoder (2021.emnlp-main)
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Shuqi Lu, Di He, Chenyan Xiong, Guolin Ke, Waleed Malik, Zhicheng Dou, Paul Bennett, Tie-Yan Liu, Arnold Overwijk
| Challenge: | Dense retrieval requires high-quality text sequence embeddings to support effective search in the representation space. |
| Approach: | They propose a self-learning method that pre-trains the autoencoder using a weak decoder to push the encoder to provide better sequence representations. |
| Outcome: | The proposed model significantly boosts the effectiveness and few-shot ability of dense retrieval models on web search, news recommendation, and open domain question answering. |
Utilizing Semantic Textual Similarity for Clinical Survey Data Feature Selection (2025.findings-acl)
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| Challenge: | Survey data often contains many features that lead to high-dimensional inputs for machine learning models. |
| Approach: | They propose to use semantic textual similarity scores to score features in surveys . they compare STS scores to a statistically-based scoring approach . |
| Outcome: | The proposed method can be used in clinical surveys to score features . it can be applied to a clinical dataset and to NIH All of Us dataset . |