1 code implementation • EMNLP 2021 • Qingbin Liu, Pengfei Cao, Cao Liu, Jiansong Chen, Xunliang Cai, Fan Yang, Shizhu He, Kang Liu, Jun Zhao
This paradigm is often impractical in real-world applications since online dialogue systems usually involve continually emerging new data and domains.
no code implementations • SemEval (NAACL) 2022 • Cong Chen, Jiansong Chen, Cao Liu, Fan Yang, Guanglu Wan, Jinxiong Xia
Furthermore, we leverage two data augment strategies and auxiliary tasks to improve the performance on few-label data and zero-shot cross-lingual settings.
1 code implementation • 31 Aug 2023 • Zhongtao Jiang, Yuanzhe Zhang, Cao Liu, Jiansong Chen, Jun Zhao, Kang Liu
As the key to sentiment analysis, sentiment composition considers the classification of a constituent via classifications of its contained sub-constituents and rules operated on them.
no code implementations • TMM 2022 • Zhao Xie, Jiansong Chen, Kewei Wu, Dan Guo, Richang Hong
In the global aggregation module, the global prior knowledge is learned by aggregating the visual feature sequence of video into a global vector.
Ranked #62 on Action Recognition on Something-Something V2
no code implementations • 17 Mar 2022 • Yantao Gong, Cao Liu, Fan Yang, Xunliang Cai, Guanglu Wan, Jiansong Chen, Weipeng Zhang, Houfeng Wang
Experiments on the open datasets verify that our model outperforms the existing calibration methods and achieves a significant improvement on the calibration metric.
no code implementations • 24 Aug 2021 • Yantao Gong, Cao Liu, Jiazhen Yuan, Fan Yang, Xunliang Cai, Guanglu Wan, Jiansong Chen, Ruiyao Niu, Houfeng Wang
To handle this problem, we propose a density-based dynamic curriculum learning model.
no code implementations • ACL 2021 • Shan Wu, Bo Chen, Chunlei Xin, Xianpei Han, Le Sun, Weipeng Zhang, Jiansong Chen, Fan Yang, Xunliang Cai
During synchronous decoding: the utterance paraphrasing is constrained by the structure of the logical form, therefore the canonical utterance can be paraphrased controlledly; the semantic decoding is guided by the semantics of the canonical utterance, therefore its logical form can be generated unsupervisedly.