Tamber, M. S., Pradeep, R., & Lin, J. (2023). Pre-Processing Matters! Improved Wikipedia Corpora for Open-Domain Question Answering Presented at the Pre-Processing Matters! Improved Wikipedia Corpora for Open-Domain Question Answering conference. https://doi.org/10.1007/978-3-031-28241-6_11
References
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Esmaeilzadeh, A., Golab, L., & Taghva, K. (2023). InfoMoD: Information-Theoretic Model Diagnostics Presented at the Paper InfoMoD: Information-Theoretic Model Diagnostics conference. https://doi.org/10.1145/3603719.3603725
Ozsu, T. (2023). Data Science: A Systematic Treatment ArXiv, abs/2301.13761. https://doi.org/10.48550/arXiv.2301.13761
Thakur, N., Ni, J., Abrego, G. H. andez \, Wieting, J., Lin, J., & Cer, D. (2023). Leveraging LLMs for Synthesizing Training Data Across Many Languages In Multilingual Dense Retrieval ArXiv, abs/2311.05800. https://doi.org/10.48550/ARXIV.2311.05800
Buchanan, G. R., McKay, D., & Clarke, C. (2023). Made to Measure: A Workshop on Human-Centred Metrics for Information Seeking Presented at the Made to Measure: A Workshop on Human-Centred Metrics for Information Seeking conference. https://doi.org/10.1145/3576840.3578301
Rorseth, J., Godfrey, P., Golab, L., Kargar, M., Srivastava, D., & Szlichta, J. (2023). CREDENCE: Counterfactual Explanations for Document Ranking ArXiv, abs/2302.04983. https://doi.org/10.48550/arXiv.2302.04983
Mackenzie, J., Trotman, A., & Lin, J. (2023). Efficient Document-at-a-Time and Score-at-a-Time Query Evaluation For Learned Sparse Representations ACM Transactions on Information Systems (TOIS), 41, 1-96. https://doi.org/10.1145/3576922
Pradeep, R., Chen, H., Gu, L., Tamber, M. S., & Lin, J. (2023). PyGaggle: A Gaggle of Resources for Open-Domain Question Answering Presented at the PyGaggle: A Gaggle of Resources for Open-Domain Question Answering conference. https://doi.org/10.1007/978-3-031-28241-6_10
Zeng, L., Zou, L., & Ozsu, T. (2023). SGSI - A Scalable GPU-Friendly Subgraph Isomorphism Algorithm IEEE Transactions on Knowledge and Data Engineering (TKDE), 35, 11899-11916. https://doi.org/10.1109/TKDE.2022.3230744
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