no code implementations • 24 May 2023 • Zhengwei Tao, Zhi Jin, Xiaoying Bai, Haiyan Zhao, Yanlin Feng, Jia Li, Wenpeng Hu
In this paper, we propose an overarching framework for event semantic processing, encompassing understanding, reasoning, and prediction, along with their fine-grained aspects.
no code implementations • NeurIPS 2021 • Qi Qin, Wenpeng Hu, Han Peng, Dongyan Zhao, Bing Liu
Continual learning (CL) of a sequence of tasks is often accompanied with the catastrophic forgetting(CF) problem.
no code implementations • 29 Sep 2021 • Mengyu Wang, Yijia Shao, Haowei Lin, Wenpeng Hu, Bing Liu
Recently, contrastive loss with data augmentation and pseudo class creation has been shown to produce markedly better results for out-of-distribution (OOD) detection than previous methods.
1 code implementation • NeurIPS 2020 • Wenpeng Hu, Mengyu Wang, Qi Qin, Jinwen Ma, Bing Liu
Existing neural network based one-class learning methods mainly use various forms of auto-encoders or GAN style adversarial training to learn a latent representation of the given one class of data.
no code implementations • COLING 2020 • Wenpeng Hu, Ran Le, Bing Liu, Jinwen Ma, Dongyan Zhao, Rui Yan
Understanding neural models is a major topic of interest in the deep learning community.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Qi Qin, Wenpeng Hu, Bing Liu
It proposes a new lifelong learning model (called L2PG) that can retain and selectively transfer the knowledge learned in the past to help learn the new task.
no code implementations • 23 Sep 2020 • Qi Qin, Wenpeng Hu, Bing Liu
In this paper, we propose a significantly more effective approach that converts the original problem to a pair-wise matching problem and then outputs how probable two instances belong to the same class.
no code implementations • ACL 2020 • Qi Qin, Wenpeng Hu, Bing Liu
In this paper, we propose a novel angle to further improve this representation learning, i. e., feature projection.
1 code implementation • 7 Nov 2019 • Zhenxin Fu, Feng Ji, Wenpeng Hu, Wei Zhou, Dongyan Zhao, Haiqing Chen, Rui Yan
Information-seeking conversation system aims at satisfying the information needs of users through conversations.
1 code implementation • COLING 2020 • Wenpeng Hu, Mengyu Wang, Bing Liu, Feng Ji, Haiqing Chen, Dongyan Zhao, Jinwen Ma, Rui Yan
The key idea of the proposed approach is to use a Forward Transformation to transform dense representations to sparse representations.
no code implementations • IJCNLP 2019 • Ran Le, Wenpeng Hu, Mingyue Shang, Zhenjun You, Lidong Bing, Dongyan Zhao, Rui Yan
Previous research on dialogue systems generally focuses on the conversation between two participants, yet multi-party conversations which involve more than two participants within one session bring up a more complicated but realistic scenario.
no code implementations • IJCNLP 2019 • Zhangming Chan, Juntao Li, Xiaopeng Yang, Xiuying Chen, Wenpeng Hu, Dongyan Zhao, Rui Yan
In this work, we improve the WAE for response generation.
no code implementations • 25 Sep 2019 • Wenpeng Hu, Ran Le, Bing Liu, Feng Ji, Haiqing Chen, Dongyan Zhao, Jinwen Ma, Rui Yan
Positive-unlabeled (PU) learning learns a binary classifier using only positive and unlabeled examples without labeled negative examples.
no code implementations • 14 Aug 2019 • Hongyin Zhu, Wenpeng Hu, Yi Zeng
Named entity recognition (NER) is a foundational technology for information extraction.
1 code implementation • ACL 2019 • Chongyang Tao, Wei Wu, Can Xu, Wenpeng Hu, Dongyan Zhao, Rui Yan
Currently, researchers have paid great attention to retrieval-based dialogues in open-domain.
Ranked #12 on Conversational Response Selection on E-commerce
1 code implementation • 31 May 2019 • Wenpeng Hu, Zhangming Chan, Bing Liu, Dongyan Zhao, Jinwen Ma, Rui Yan
Existing neural models for dialogue response generation assume that utterances are sequentially organized.
no code implementations • ICLR 2019 • Wenpeng Hu, Zhou Lin, Bing Liu, Chongyang Tao, Zhengwei Tao, Jinwen Ma, Dongyan Zhao, Rui Yan
Several continual learning methods have been proposed to address the problem.
no code implementations • ICLR 2019 • Wenpeng Hu, Zhengwei Tao, Zhanxing Zhu, Bing Liu, Zhou Lin, Jinwen Ma, Dongyan Zhao, Rui Yan
A large amount of parallel data is needed to train a strong neural machine translation (NMT) system.
no code implementations • ICLR 2018 • Wenpeng Hu, Bing Liu, Rui Yan, Dongyan Zhao, Jinwen Ma
In the paper, we propose a new question generation problem, which also requires the input of a target topic in addition to a piece of descriptive text.
no code implementations • ACL 2017 • Long Zhou, Wenpeng Hu, Jiajun Zhang, Cheng-qing Zong
Neural machine translation (NMT) becomes a new approach to machine translation and generates much more fluent results compared to statistical machine translation (SMT).
no code implementations • COLING 2016 • Wenpeng Hu, Jiajun Zhang, Nan Zheng
Recent work for learning word representations has applied successfully to many NLP applications, such as sentiment analysis and question answering.