Li, M. ., Li, M. ., Xiong, K. ., & Lin, J. . (2021). Multi-Task Dense Retrieval via Model Uncertainty Fusion for Open-Domain Question Answering. Multi-Task Dense Retrieval via Model Uncertainty Fusion for Open-Domain Question Answering. Presented at the. Retrieved from https://aclanthology.org/2021.findings-emnlp.26
Reference author: Minghan Li
First name
Minghan
Last name
Li
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
Li, M. ., Li, M. ., Xiong, K. ., & Lin, J. . (2021). Multi-Task Dense Retrieval via Model Uncertainty Fusion for Open-Domain Question Answering. Multi-Task Dense Retrieval via Model Uncertainty Fusion for Open-Domain Question Answering . Presented at the. Retrieved from https://aclanthology.org/2021.findings-emnlp.26
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
Li, M. ., Zhang, X. ., Xin, J. ., Zhang, H. ., & Lin, J. . (2022). Certified Error Control of Candidate Set Pruning for Two-Stage Relevance Ranking. ArXiv, abs/2205.09638. https://doi.org/10.48550/arXiv.2205.09638
Lin, S.-C. ., Li, M. ., & Lin, J. . (2022). Aggretriever: A Simple Approach to Aggregate Textual Representation For Robust Dense Passage Retrieval. ArXiv, abs/2208.00511. https://doi.org/10.48550/arXiv.2208.00511
Lin, S.-C. ., Li, M. ., & Lin, J. . (2022). Aggretriever: A Simple Approach to Aggregate Textual Representation For Robust Dense Passage Retrieval. ArXiv, abs/2208.00511. https://doi.org/10.48550/arXiv.2208.00511
Liu, L. ., Li, M. ., Lin, J. ., Riedel, S. ., & Stenetorp, P. . (2022). Query Expansion Using Contextual Clue Sampling With Language Models. ArXiv, abs/2210.07093. https://doi.org/10.48550/arXiv.2210.07093
Li, M. ., Lin, S.-C. ., Oguz, B. ., Ghoshal, A. ., Lin, J. ., Mehdad, Y. ., Yih, W.- tau ., & Chen, X. . (2022). CITADEL: Conditional Token Interaction via Dynamic Lexical Routing For Efficient and Effective Multi-Vector Retrieval. ArXiv, abs/2211.10411. https://doi.org/10.48550/arXiv.2211.10411