no code implementations • 13 Sep 2021 • Meiqi Chen, Yuan Zhang, Xiaoyu Kou, Yuntao Li, Yan Zhang
To tackle this issue, we propose r-GAT, a relational graph attention network to learn multi-channel entity representations.
no code implementations • 28 Oct 2020 • Xiaoyu Kou, Yankai Lin, Yuntao Li, Jiahao Xu, Peng Li, Jie zhou, Yan Zhang
Knowledge graph embedding (KGE), aiming to embed entities and relations into low-dimensional vectors, has attracted wide attention recently.
1 code implementation • EMNLP 2020 • Xiaoyu Kou, Yankai Lin, Shaobo Liu, Peng Li, Jie zhou, Yan Zhang
Graph embedding (GE) methods embed nodes (and/or edges) in graph into a low-dimensional semantic space, and have shown its effectiveness in modeling multi-relational data.
1 code implementation • 18 Aug 2020 • Xiaoyu Kou, Bingfeng Luo, Huang Hu, Yan Zhang
While various forms of models are proposed for the link prediction task, most of them are designed based on a few known relation patterns in several well-known datasets.
1 code implementation • 8 Apr 2020 • Yiren Chen, Xiaoyu Kou, Jiangang Bai, Yunhai Tong
One of the most popular paradigms of applying large pre-trained NLP models such as BERT is to fine-tune it on a smaller dataset.
no code implementations • 23 Dec 2019 • Yujing Wang, Yaming Yang, Yiren Chen, Jing Bai, Ce Zhang, Guinan Su, Xiaoyu Kou, Yunhai Tong, Mao Yang, Lidong Zhou
Learning text representation is crucial for text classification and other language related tasks.
3 code implementations • 10 Jun 2019 • Hansheng Ren, Bixiong Xu, Yujing Wang, Chao Yi, Congrui Huang, Xiaoyu Kou, Tony Xing, Mao Yang, Jie Tong, Qi Zhang
At Microsoft, we develop a time-series anomaly detection service which helps customers to monitor the time-series continuously and alert for potential incidents on time.