no code implementations • 4 Mar 2024 • Chen Xu, Tian Lan, Changlong Yu, Wei Wang, Jun Gao, Yu Ji, Qunxi Dong, Kun Qian, Piji Li, Wei Bi, Bin Hu
Lexicon-based constrained decoding approaches aim to control the meaning or style of the generated text through certain target concepts.
no code implementations • 18 Feb 2024 • Jun Gao, Huan Zhao, Wei Wang, Changlong Yu, Ruifeng Xu
In this study, we present EventRL, a reinforcement learning approach developed to enhance event extraction for large language models (LLMs).
no code implementations • 8 Oct 2023 • Jun Gao, Huan Zhao, Yice Zhang, Wei Wang, Changlong Yu, Ruifeng Xu
Information Extraction (IE) is an essential task in Natural Language Processing.
no code implementations • 7 Mar 2023 • Jun Gao, Huan Zhao, Changlong Yu, Ruifeng Xu
While ChatGPT has demonstrated impressive results in tasks like machine translation, text summarization, and question answering, it presents challenges when used for complex tasks like event extraction.
no code implementations • 6 Jan 2023 • Jun Gao, Changlong Yu, Wei Wang, Huan Zhao, Ruifeng Xu
We present Mask-then-Fill, a flexible and effective data augmentation framework for event extraction.
1 code implementation • 7 Dec 2022 • Fangqi Zhu, Jun Gao, Changlong Yu, Wei Wang, Chen Xu, Xin Mu, Min Yang, Ruifeng Xu
First, the pretrained language models adopted by current works ignore event-level knowledge, resulting in an inability to capture the correlations between events well.
1 code implementation • 15 Nov 2022 • Changlong Yu, Weiqi Wang, Xin Liu, Jiaxin Bai, Yangqiu Song, Zheng Li, Yifan Gao, Tianyu Cao, Bing Yin
Understanding users' intentions in e-commerce platforms requires commonsense knowledge.
no code implementations • 2 Nov 2022 • Haolin Deng, Yanan Zhang, Yangfan Zhang, Wangyang Ying, Changlong Yu, Jun Gao, Wei Wang, Xiaoling Bai, Nan Yang, Jin Ma, Xiang Chen, Tianhua Zhou
To the best of our knowledge, it is currently the largest manually-annotated Chinese dataset for open event extraction.
1 code implementation • 24 Oct 2022 • Changlong Yu, Tianyi Xiao, Lingpeng Kong, Yangqiu Song, Wilfred Ng
Though linguistic knowledge emerges during large-scale language model pretraining, recent work attempt to explicitly incorporate human-defined linguistic priors into task-specific fine-tuning.
1 code implementation • ACL 2022 • Jun Gao, Wei Wang, Changlong Yu, Huan Zhao, Wilfred Ng, Ruifeng Xu
Representations of events described in text are important for various tasks.
1 code implementation • Findings (ACL) 2022 • Changlong Yu, Hongming Zhang, Yangqiu Song, Wilfred Ng
Large-scale pre-trained language models have demonstrated strong knowledge representation ability.
1 code implementation • 24 Oct 2020 • Fuyu Lv, Mengxue Li, Tonglei Guo, Changlong Yu, Fei Sun, Taiwei Jin, Wilfred Ng
The offline experimental results based on real-world E-commerce data demonstrate the effectiveness and verify the importance of unclicked items in sequential recommendation.
1 code implementation • EMNLP 2020 • Changlong Yu, Jialong Han, Peifeng Wang, Yangqiu Song, Hongming Zhang, Wilfred Ng, Shuming Shi
We also demonstrate that distributional methods are ideal to make up for pattern-based ones in such cases.
no code implementations • ACL 2020 • Changlong Yu, Jialong Han, Haisong Zhang, Wilfred Ng
Hypernymy detection, a. k. a, lexical entailment, is a fundamental sub-task of many natural language understanding tasks.
1 code implementation • AKBC 2020 • Changlong Yu, Hongming Zhang, Yangqiu Song, Wilfred Ng, Lifeng Shang
Computational and cognitive studies suggest that the abstraction of eventualities (activities, states, and events) is crucial for humans to understand daily eventualities.
1 code implementation • IJCNLP 2019 • Hongming Zhang, Jiaxin Bai, Yan Song, Kun Xu, Changlong Yu, Yangqiu Song, Wilfred Ng, Dong Yu
Therefore, in this paper, we propose a multiplex word embedding model, which can be easily extended according to various relations among words.
2 code implementations • 1 Sep 2019 • Fuyu Lv, Taiwei Jin, Changlong Yu, Fei Sun, Quan Lin, Keping Yang, Wilfred Ng
In this paper, we propose a new sequential deep matching (SDM) model to capture users' dynamic preferences by combining short-term sessions and long-term behaviors.