Parsa, M. S., & Golab, L. (2021). Academic Integrity in Online Education During the COVID-19 Pandemic: A Social Media Mining Study Presented at the Academic Integrity in Online Education During the COVID-19 Pandemic: A Social Media Mining Study conference.
References
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Lin, S.-C., & Lin, J. (2021). Densifying Sparse Representations for Passage Retrieval by Representational Slicing ArXiv, abs/2112.04666. Retrieved from https://arxiv.org/abs/2112.04666
Arabzadeh, N., Yan, X., & Clarke, C. (2021). Predicting Efficiency/Effectiveness Trade-Offs for Dense vs. Sparse Retrieval Strategy Selection Presented at the Predicting Efficiency Effectiveness Trade-Offs for Dense Vs. Sparse Retrieval Strategy Selection conference. https://doi.org/10.1145/3459637.3482159
Bai, H., Shi, P., Lin, J., Tan, L., Xiong, K., Gao, W., … Li, M. (2021). Semantics of the Unwritten: The Effect of End of Paragraph and Sequence Tokens on Text Generation With GPT2 Presented at the Semantics of the Unwritten: The Effect of End of Paragraph and Sequence Tokens on Text Generation With GPT2 conference. Retrieved from https://aclanthology.org/2021.acl-srw.16
Mohapatra, S., Sasy, S., He, X., Kamath, G., & Thakkar, O. (2021). The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection ArXiv, abs/2111.04906. Retrieved from https://arxiv.org/abs/2111.04906
Lin, J., Nogueira, R., & Yates, A. (2021). Pretrained Transformers for Text Ranking: BERT and Beyond Morgan \& Claypool. https://doi.org/10.2200/S01123ED1V01Y202108HLT053
Kato, M. P., Liu, Y., Kando, N., & Clarke, C. (2021). Report on the 15th Round of NII Testbeds and Community for Information Access Research (NTCIR-15) SIGIR Forum, 55, 1-21. https://doi.org/10.1145/3527546.3527570
Lin, S.-C., Yang, J.-H., & Lin, J. (2021). Contextualized Query Embeddings for Conversational Search Presented at the Contextualized Query Embeddings for Conversational Search conference. Retrieved from https://aclanthology.org/2021.emnlp-main.77
Lin, J. (2021). A Proposed Conceptual Framework for a Representational Approach To Information Retrieval SIGIR Forum, 55, 1-4. https://doi.org/10.1145/3527546.3527552
Lin, J. (2021). A Proposed Conceptual Framework for a Representational Approach To Information Retrieval ArXiv, abs/2110.01529. Retrieved from https://arxiv.org/abs/2110.01529