![]() Zhang, Baojun, Si-Qi Liu, Chaoran Li, Erik Lykken, Shan Jiang, Elizabeth Wong, Zhihua Gong, et al. “ Inflammation-Dependent IL18 Signaling Restricts Hepatocellular Carcinoma Growth by Enhancing the Accumulation and Activity of Tumor-Infiltrating Lymphocytes.” Cancer Res 76, no. Michelotti, Rui Chen, Jianhua Sui, Bin Yang, et al. Markowitz, Geoffrey J., Pengyuan Yang, Jing Fu, Gregory A. ![]() “ Glimpse of natural selection of long-lived T-cell clones in healthy life.” Proc Natl Acad Sci U S A 113, no. Zhang, Baojun, Qingzhu Jia, Cheryl Bock, Gang Chen, Haili Yu, Qingshan Ni, Ying Wan, Qijing Li, and Yuan Zhuang. “ In Vivo Expansion and Antitumor Activity of Coinfused CD28- and 4-1BB-Engineered CAR-T Cells in Patients with B Cell Leukemia.” Mol Ther 26, no. Cheng, Zhi, Runhong Wei, Qiuling Ma, Lin Shi, Feng He, Zixiao Shi, Tao Jin, et al. “ CD36 initiates the secretory phenotype during the establishment of cellular senescence.” Embo Rep 19, no. Chong, Mengyang, Tao Yin, Rui Chen, Handan Xiang, Lifeng Yuan, Yi Ding, Christopher C. “ Late-stage tumors induce anemia and immunosuppressive extramedullary erythroid progenitor cells.” Nat Med 24, no. Zhao, Lintao, Ran He, Haixia Long, Bo Guo, Qingzhu Jia, Diyuan Qin, Si-Qi Liu, et al. “ Local mutational diversity drives intratumoral immune heterogeneity in non-small cell lung cancer.” Nat Commun 9, no. Alexander, Chengdu Sun, Zhihua Gong, Jia-Nan Cheng, et al. Jia, Qingzhu, Wei Wu, Yuqi Wang, Peter B. “ TCR repertoire and CDR3 motif analyses depict the role of αβ T cells in Ankylosing spondylitis.” Ebiomedicine 47 (September 2019): 414–26.Ħ. Zheng, Ming, Xin Zhang, Yinghui Zhou, Juan Tang, Qing Han, Yang Zhang, Qingshan Ni, et al. “ Radiation-induced eosinophils improve cytotoxic T lymphocyte recruitment and response to immunotherapy.” Sci Adv 7, no. ![]() Cheng, Jia-Nan, Wen Luo, Chengdu Sun, Zheng Jin, Xianghua Zeng, Peter B. “ Peripheral eosinophil counts predict efficacy of anti-CD19 CAR-T cell therapy against B-lineage non-Hodgkin lymphoma.” Theranostics 11, no. Jia, Qingzhu, Diyuan Qin, Feng He, Qichao Xie, Zhitao Ying, Yajing Zhang, Yuqin Song, et al. “ Conversion of effector CD4+ T cells to a CD8+ MHC II-recognizing lineage.” Cell Mol Immunol 18, no. Robins, Elizabeth, Ming Zheng, Qingshan Ni, Siqi Liu, Chen Liang, Baojun Zhang, Jian Guo, et al. “ Resident memory T cells in tumor-distant tissues fortify against metastasis formation.” Cell Rep 35, no. Christian, Laura S., Liuyang Wang, Bryan Lim, Dachuan Deng, Haiyang Wu, Xiao-Fan Wang, and Qi-Jing Li. Hopefully, our preliminary observations can deepen the understanding of larger-context training and enlighten more follow-up works on the use of contextual information.1. Experimentally, we set up a testbed based on four tagging tasks and thirteen datasets. To this end, we conduct a thorough comparative study on four proposed aggregators for context information collecting and present an attribute-aided evaluation method to interpret the improvement brought by larger-context training. In this paper, instead of pursuing a state-of-the-art tagging system by architectural exploration, we focus on investigating when and why the larger-context training, as a general strategy, can work. ![]() Although several existing works have attempted to shift tagging systems from sentence-level to document-level, there is still no consensus conclusion about when and why it works, which limits the applicability of the larger-context approach in tagging tasks. However, a relatively less discussed topic is what if more context information is introduced into current top-scoring tagging systems. The development of neural networks and pretraining techniques has spawned many sentence-level tagging systems that achieved superior performance on typical benchmarks.
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