1 code implementation • 30 Jan 2024 • Xurui Li, Ziming Huang, Feng Xue, Yu Zhou
We reveal that the abundant normal and abnormal cues implicit in unlabeled test images can be exploited for anomaly determination, which is ignored by prior methods.
no code implementations • COLING 2022 • Ziming Huang, Zhuoxuan Jiang, Ke Wang, Juntao Li, Shanshan Feng, Xian-Ling Mao
Although most existing methods can fulfil this requirement, they can only model single-source dialog data and cannot effectively capture the underlying knowledge of relations among data and subtasks.
no code implementations • 2 Dec 2021 • Zixuan Yuan, Yada Zhu, Wei zhang, Ziming Huang, Guangnan Ye, Hui Xiong
Earnings call (EC), as a periodic teleconference of a publicly-traded company, has been extensively studied as an essential market indicator because of its high analytical value in corporate fundamentals.
no code implementations • ACL 2021 • Wei zhang, Ziming Huang, Yada Zhu, Guangnan Ye, Xiaodong Cui, Fan Zhang
In the recent advances of natural language processing, the scale of the state-of-the-art models and datasets is usually extensive, which challenges the application of sample-based explanation methods in many aspects, such as explanation interpretability, efficiency, and faithfulness.
no code implementations • 9 Jun 2021 • Wei zhang, Ziming Huang, Yada Zhu, Guangnan Ye, Xiaodong Cui, Fan Zhang
In the recent advances of natural language processing, the scale of the state-of-the-art models and datasets is usually extensive, which challenges the application of sample-based explanation methods in many aspects, such as explanation interpretability, efficiency, and faithfulness.
no code implementations • 11 Nov 2019 • Zhuoxuan Jiang, Ziming Huang, Dong Sheng Li, Xian-Ling Mao
In this paper, we propose a novel joint end-to-end model by multi-task representation learning, which can capture the knowledge from heterogeneous information through automatically learning knowledgeable low-dimensional embeddings from data, named with DialogAct2Vec.
no code implementations • WS 2019 • Zhuoxuan Jiang, Xian-Ling Mao, Ziming Huang, Jie Ma, Shaochun Li
Learning an efficient manager of dialogue agent from data with little manual intervention is important, especially for goal-oriented dialogues.