1 code implementation • 9 Dec 2020 • Weibo Zhang, Guihua Liu, Zhuohua Li, Fuqing Zhu
In order to further integrate modal information, we investigate a candidate solution based on complementary visual and linguistic network in Hateful Memes Challenge 2020.
no code implementations • COLING 2020 • Shangwen Lv, Fuqing Zhu, Songlin Hu
In the knowledge retrieval stage, we select relevant external event knowledge from ASER.
1 code implementation • 25 May 2020 • Dongjun Wei, Yaxin Liu, Fuqing Zhu, Liangjun Zang, Wei Zhou, Yijun Lu, Songlin Hu
In this paper, a novel integration method called AutoSUM is proposed for automatic feature extraction and multi-user preference simulation to overcome the drawbacks of previous methods.
no code implementations • 12 Apr 2020 • Shangwen Lv, Yuechen Wang, Daya Guo, Duyu Tang, Nan Duan, Fuqing Zhu, Ming Gong, Linjun Shou, Ryan Ma, Daxin Jiang, Guihong Cao, Ming Zhou, Songlin Hu
In this work, we introduce a learning algorithm which directly optimizes model's ability to learn text representations for effective learning of downstream tasks.
1 code implementation • IJCNLP 2019 • Chunyuan Yuan, Wei Zhou, Mingming Li, Shangwen Lv, Fuqing Zhu, Jizhong Han, Songlin Hu
Existing works mainly focus on matching candidate responses with every context utterance on multiple levels of granularity, which ignore the side effect of using excessive context information.
Ranked #5 on Conversational Response Selection on RRS
2 code implementations • 25 May 2019 • Dongjun Wei, Yaxin Liu, Fuqing Zhu, Liangjun Zang, Wei Zhou, Jizhong Han, Songlin Hu
Entity summarization aims at creating brief but informative descriptions of entities from knowledge graphs.
no code implementations • 17 Nov 2017 • Fuqing Zhu, Xiangwei Kong, Haiyan Fu, Qi Tian
A small proportion of these retrieved samples are randomly selected as the Pseudo Positive samples and added to the target training set for the supervised CNN training.
no code implementations • 5 May 2017 • Fuqing Zhu, Xiangwei Kong, Liang Zheng, Haiyan Fu, Qi Tian
In the experiment, we show that the proposed Part-based Deep Hashing method yields very competitive re-id accuracy on the large-scale Market-1501 and Market-1501+500K datasets.