Zhong, W., Xie, Y., & Lin, J. (2023). Answer Retrieval for Math Questions Using Structural and Dense Retrieval Presented at the Retrieval for Math Questions Using Structural and Dense Retrieval conference. https://doi.org/10.1007/978-3-031-42448-9_18
References
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2023
Yang, J.-H., Lassance, C., de Rezende, R. S., Srinivasan, K., Redi, M., Clinchant, S. ephane, & Lin, J. (2023). AToMiC: An Image/Text Retrieval Test Collection to Support Multimedia Content Creation ArXiv, abs/2304.01961. https://doi.org/10.48550/arXiv.2304.01961
Ehrlinger, L., Harmouch, H., Ilyas, I., & Naumann, F. (2023). Preface QDB Presented at the Paper Preface QDB conference. Retrieved from https://ceur-ws.org/Vol-3462/QDB0.pdf
Li, M., Zhuang, H., Hui, K., Qin, Z., Lin, J., Jagerman, R., … Bendersky, M. (2023). Generate, Filter, and Fuse: Query Expansion via Multi-Step Keyword Generation for Zero-Shot Neural Rankers ArXiv, abs/2311.09175. https://doi.org/10.48550/ARXIV.2311.09175
Hebert, L., Chen, H. Y., Cohen, R., & Golab, L. (2023). Qualitative Analysis of a Graph Transformer Approach to Addressing Hate Speech: Adapting to Dynamically Changing Content ArXiv, abs/2301.10871. https://doi.org/10.48550/arXiv.2301.10871
Zhang, X., Li, M., & Lin, J. (2023). Improving Out-of-Distribution Generalization of Neural Rerankers With Contextualized Late Interaction ArXiv, abs/2302.06589. https://doi.org/10.48550/arXiv.2302.06589
Lin, S.-C., & Lin, J. (2023). A Dense Representation Framework for Lexical and Semantic Matching ACM Transactions on Information Systems (TOIS), 41, 1-110. https://doi.org/10.1145/3582426
Lin, S.-C., Asai, A., Li, M., Oguz, B., Lin, J., Mehdad, Y., … Chen, X. (2023). How to Train Your Dragon: Diverse Augmentation Towards Generalizable Dense Retrieval Presented at the How to Train Your Dragon: Diverse Augmentation Towards Generalizable Dense Retrieval conference. Retrieved from https://aclanthology.org/2023.findings-emnlp.423
Hebert, L., Golab, L., Poupart, P., & Cohen, R. (2023). FedFormer: Contextual Federation With Attention in Reinforcement Learning Presented at the FedFormer: Contextual Federation With Attention in Reinforcement Learning conference. https://doi.org/10.5555/3545946.3598716
Kassaie, B., & Tompa, F. (2023). Autonomously Computable Information Extraction Proceedings of the VLDB Endowment (PVLDB), 16, 2431-2443. https://doi.org/10.14778/3603581.3603585