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 Presented at the On the Separation of Logical and Physical Ranking Models for Text Retrieval Applications conference. 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 Presented at the Simple and Effective Unsupervised Redundancy Elimination to Compress Dense Vectors for Passage Retrieval conference. 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 Presented at the Simple and Effective Unsupervised Redundancy Elimination to Compress Dense Vectors for Passage Retrieval conference. 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 Presented at the Another Look at DPR: Reproduction of Training and Replication Of Retrieval conference. 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