Reference author: Xueguang Ma

Lin, J. ., Ma, X. ., Mackenzie, J. ., & Mallia, A. . (2021). On the Separation of Logical and Physical Ranking Models for Text Retrieval Applications. On the Separation of Logical and Physical Ranking Models for Text Retrieval Applications . Presented at the. Retrieved from http://ceur-ws.org/Vol-2950/paper-26.pdf

Ma, X. ., Li, M. ., Sun, K. ., Xin, J. ., & Lin, J. . (2021). Simple and Effective Unsupervised Redundancy Elimination to Compress Dense Vectors for Passage Retrieval. Simple and Effective Unsupervised Redundancy Elimination to Compress Dense Vectors for Passage Retrieval. Presented at the. Retrieved from https://aclanthology.org/2021.emnlp-main.227

Ma, X. ., Li, M. ., Sun, K. ., Xin, J. ., & Lin, J. . (2021). Simple and Effective Unsupervised Redundancy Elimination to Compress Dense Vectors for Passage Retrieval. Simple and Effective Unsupervised Redundancy Elimination to Compress Dense Vectors for Passage Retrieval. Presented at the. Retrieved from https://aclanthology.org/2021.emnlp-main.227

Ma, X. ., Sun, K. ., Pradeep, R. ., Li, M. ., & Lin, J. . (2022). Another Look at DPR: Reproduction of Training and Replication Of Retrieval. Another Look at DPR: Reproduction of Training and Replication Of Retrieval. Presented at the. https://doi.org/10.1007/978-3-030-99736-6_41

Yang, J.-H. ., Ma, X. ., & Lin, J. . (2021). Sparsifying Sparse Representations for Passage Retrieval by Top-K Masking. ArXiv, abs/2112.09628. Retrieved from https://arxiv.org/abs/2112.09628

Li, H. ., Zhuang, S. ., Mourad, A. ., Ma, X. ., Lin, J. ., & Zuccon, G. . (2021). Improving Query Representations for Dense Retrieval With Pseudo Relevance Feedback: A Reproducibility Study. ArXiv, abs/2112.06400. Retrieved from https://arxiv.org/abs/2112.06400

Li, H. ., Wang, S. ., Zhuang, S. ., Mourad, A. ., Ma, X. ., Lin, J. ., & Zuccon, G. . (2022). To Interpolate or Not to Interpolate: PRF, Dense and Sparse Retrievers. ArXiv, abs/2205.00235. https://doi.org/10.48550/arXiv.2205.00235

Zhang, X. ., Ogueji, K. ., Ma, X. ., & Lin, J. . (2022). Towards Best Practices for Training Multilingual Dense Retrieval Models. ArXiv, abs/2204.02363. https://doi.org/10.48550/arXiv.2204.02363

Gao, L. ., Ma, X. ., Lin, J. ., & Callan, J. . (2022). Tevatron: An Efficient and Flexible Toolkit for Dense Retrieval. ArXiv, abs/2203.05765. https://doi.org/10.48550/arXiv.2203.05765

Li, H. ., Wang, S. ., Zhuang, S. ., Mourad, A. ., Ma, X. ., Lin, J. ., & Zuccon, G. . (2022). To Interpolate or Not to Interpolate: PRF, Dense and Sparse Retrievers. ArXiv, abs/2205.00235. https://doi.org/10.48550/arXiv.2205.00235