Ma, X. ., Teofili, T. ., & Lin, J. . (2023). Anserini Gets Dense Retrieval: Integration of Lucene\textquoterights HNSW Indexes. ArXiv, abs/2304.12139. https://doi.org/10.48550/arXiv.2304.12139
Publications
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Hebert, L. ., Golab, L. ., Poupart, P. ., & Cohen, R. . (2023). FedFormer: Contextual Federation With Attention in Reinforcement Learning. FedFormer: Contextual Federation With Attention in Reinforcement Learning. Presented at the. https://doi.org/10.5555/3545946.3598716
Wu, Z. ., Deshmukh, A. A., Wu, Y. ., Lin, J. ., & Mou, L. . (2023). Unsupervised Chunking With Hierarchical RNN. ArXiv, abs/2309.04919. https://doi.org/10.48550/arXiv.2309.04919
Hebert, L. ., Golab, L. ., & Cohen, R. . (2023). Predicting Hateful Discussions on Reddit Using Graph Transformer Networks And Communal Context. ArXiv, abs/2301.04248. https://doi.org/10.48550/arXiv.2301.04248
Bayat, F. F., Qian, K. ., Han, B. ., Sang, Y. ., Belyi, A. ., Khorshidi, S. ., Wu, F. ., Ilyas, I. ., & Li, Y. . (2023). FLEEK: Factual Error Detection and Correction With Evidence Retrieved From External Knowledge. ArXiv, abs/2310.17119. https://doi.org/10.48550/ARXIV.2310.17119
Salem, K. . (2023). TECHNICAL PERSPECTIVE: Ad Hoc Transactions: What They Are And Why We Should Care. SIGMOD Record, 52, 6. https://doi.org/10.1145/3604437.3604439
Lin, J. ., Alfonso-Hermelo, D. ., Jeronymo, V. ., Kamalloo, E. ., Lassance, C. ., Nogueira, R. F., Ogundepo, O. ., Rezagholizadeh, M. ., Thakur, N. ., Yang, J.-H. ., & Zhang, X. . (2023). Simple Yet Effective Neural Ranking and Reranking Baselines for Cross-Lingual Information Retrieval. ArXiv, abs/2304.01019. https://doi.org/10.48550/arXiv.2304.01019
Zou, L. ., Pang, Y. ., Ozsu, T. ., & Chen, J. . (2023). Efficient Execution of SPARQL Queries With OPTIONAL and UNION Expressions. ArXiv, abs/2303.13844. https://doi.org/10.48550/arXiv.2303.13844
Salihoglu, S. . (2023). K\ uzu: A Database Management System for "Beyond Relational" Workloads. SIGMOD Record, 52, 39-40. https://doi.org/10.1145/3631504.3631514
Lin, S.-C. ., Asai, A. ., Li, M. ., Oguz, B. ., Lin, J. ., Mehdad, Y. ., Yih, W.- tau ., & Chen, X. . (2023). How to Train Your Dragon: Diverse Augmentation Towards Generalizable Dense Retrieval. How to Train Your Dragon: Diverse Augmentation Towards Generalizable Dense Retrieval. Presented at the. Retrieved from https://aclanthology.org/2023.findings-emnlp.423