no code implementations • ACL (ECNLP) 2021 • Haoran Shi, Zhibiao Rao, Yongning Wu, Zuohua Zhang, Chu Wang
In this paper, we propose a keyword augmentation method based on generative seq2seq model and trie-based search mechanism, which is able to generate high-quality keywords for any products or product lists.
no code implementations • 9 Oct 2023 • Xiong-Hui Chen, Junyin Ye, Hang Zhao, Yi-Chen Li, Haoran Shi, Yu-Yan Xu, Zhihao Ye, Si-Hang Yang, Anqi Huang, Kai Xu, Zongzhang Zhang, Yang Yu
In this work, we focus on imitator learning based on only one expert demonstration.
1 code implementation • EMNLP 2020 • Zhengzhong Liu, Guanxiong Ding, Avinash Bukkittu, Mansi Gupta, Pengzhi Gao, Atif Ahmed, Shikun Zhang, Xin Gao, Swapnil Singhavi, Linwei Li, Wei Wei, Zecong Hu, Haoran Shi, Haoying Zhang, Xiaodan Liang, Teruko Mitamura, Eric P. Xing, Zhiting Hu
Empirical natural language processing (NLP) systems in application domains (e. g., healthcare, finance, education) involve interoperation among multiple components, ranging from data ingestion, human annotation, to text retrieval, analysis, generation, and visualization.
no code implementations • 20 Mar 2019 • Lu-chen Liu, Haoran Li, Zhiting Hu, Haoran Shi, Zichang Wang, Jian Tang, Ming Zhang
Our model learns hierarchical representationsof event sequences, to adaptively distinguish between short-range and long-range events, and accurately capture coretemporal dependencies.
no code implementations • 27 Sep 2018 • Wentao Wang, Zhiting Hu, Zichao Yang, Haoran Shi, Eric P. Xing
Neural text generation models such as recurrent networks are typically trained by maximizing data log-likelihood based on cross entropy.
4 code implementations • ACL 2019 • Zhiting Hu, Haoran Shi, Bowen Tan, Wentao Wang, Zichao Yang, Tiancheng Zhao, Junxian He, Lianhui Qin, Di Wang, Xuezhe Ma, Zhengzhong Liu, Xiaodan Liang, Wangrong Zhu, Devendra Singh Sachan, Eric P. Xing
The versatile toolkit also fosters technique sharing across different text generation tasks.
no code implementations • WS 2018 • Zhiting Hu, Zichao Yang, Tiancheng Zhao, Haoran Shi, Junxian He, Di Wang, Xuezhe Ma, Zhengzhong Liu, Xiaodan Liang, Lianhui Qin, Devendra Singh Chaplot, Bowen Tan, Xingjiang Yu, Eric Xing
The features make Texar particularly suitable for technique sharing and generalization across different text generation applications.
no code implementations • 11 Nov 2017 • Haoran Shi, Pengtao Xie, Zhiting Hu, Ming Zhang, Eric P. Xing
Considering the complicated and dedicated process to assign correct codes to each patient admission based on overall diagnosis, we propose a hierarchical deep learning model with attention mechanism which can automatically assign ICD diagnostic codes given written diagnosis.