图谱构建小组

综述

1. 刘峤, 李杨, 段宏,等. 知识图谱构建技术综述[J]. 计算机研究与发展, 2016, 53(3):582-600.paper
2. 徐增林, 盛泳潘, 贺丽荣,等. 知识图谱技术综述[J]. 电子科技大学学报, 2016, 45(4):589-606.paper
3. 李舟军, 范宇, 吴贤杰. 面向自然语言处理的预训练技术研究综述[J]. 计算机科学, 2020, v.47(03):170-181.paper

预训练模型

1. Huang Z, Xu W, Yu K. Bidirectional LSTM-CRF models for sequence tagging[J]. arXiv preprint arXiv:1508.01991, 2015.paper
2. Vaswani A , Shazeer N , Parmar N , et al. Attention Is All You Need[J]. arXiv, 2017.paper
3. Devlin J , Chang M W , Lee K , et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[J]. 2018.paper
4. Liu Y , Ott M , Goyal N , et al. RoBERTa: A Robustly Optimized BERT Pretraining Approach[J]. 2019.paper
5. Sun Y, Wang S, Li Y, et al. Ernie: Enhanced representation through knowledge integration[J]. arXiv preprint arXiv:1904.09223, 2019.paper
6. Zhang Z, Han X, Liu Z, et al. ERNIE: Enhanced language representation with informative entities[J]. arXiv preprint arXiv:1905.07129, 2019.paper
7. Sun Y, Wang S, Li Y K, et al. ERNIE 2.0: A Continual Pre-Training Framework for Language Understanding[C]//AAAI. 2020: 8968-8975.paper
8. Cui Y, Che W, Liu T, et al. Pre-training with whole word masking for chinese bert[J]. arXiv preprint arXiv:1906.08101, 2019.paper
9. Jiao X, Yin Y, Shang L, et al. Tinybert: Distilling bert for natural language understanding[J]. arXiv preprint arXiv:1909.10351, 2019.paper
10. Yang Z , Dai Z , Yang Y , et al. XLNet: Generalized Autoregressive Pretraining for Language Understanding[J]. 2019.paper