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 • 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 • 28 Feb 2024 • Mengjie Ren, Boxi Cao, Hongyu Lin, Cao Liu, Xianpei Han, Ke Zeng, Guanglu Wan, Xunliang Cai, Le Sun
Instruction Fine-tuning~(IFT) is a critical phase in building large language models~(LLMs).
1 code implementation • 16 Oct 2023 • Zhongtao Jiang, Yuanzhe Zhang, Cao Liu, Jun Zhao, Kang Liu
In this paper, we for the first time theoretically and empirically identify that such a paradox is mainly due to the label shift of the in-context model to the data distribution, in which LLMs shift the label marginal $p(y)$ while having a good label conditional $p(x|y)$.
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 • 19 Jun 2022 • Pengfei Zhang, Xiaohui Hu, Kaidong Yu, Jian Wang, Song Han, Cao Liu, Chunyang Yuan
Firstly, we build an evaluation metric composed of 5 groups of parallel sub-metrics called Multi-Metric Evaluation (MME) to evaluate the quality of dialogue comprehensively.
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 • CONLL 2019 • Cao Liu, Kang Liu, Shizhu He, Zaiqing Nie, Jun Zhao
Facing this challenge, we present a response generation model which incorporates Interlocutor-aware Contexts into Recurrent Encoder-Decoder frameworks (ICRED) for RGMPC.
no code implementations • IJCNLP 2019 • Cao Liu, Kang Liu, Shizhu He, Zaiqing Nie, Jun Zhao
Meanwhile, such generated question can express the given predicate and correspond to a definitive answer.
no code implementations • ACL 2019 • Cao Liu, Shizhu He, Kang Liu, Jun Zhao
To tackle the above two problems, we present a Vocabulary Pyramid Network (VPN) which is able to incorporate multi-pass encoding and decoding with multi-level vocabularies into response generation.
no code implementations • IJCNLP 2017 • Shangmin Guo, Kang Liu, Shizhu He, Cao Liu, Jun Zhao, Zhuoyu Wei
The IJCNLP-2017 Multi-choice Question Answering(MCQA) task aims at exploring the performance of current Question Answering(QA) techniques via the realworld complex questions collected from Chinese Senior High School Entrance Examination papers and CK12 website1.
no code implementations • ACL 2017 • Shizhu He, Cao Liu, Kang Liu, Jun Zhao
Generating answer with natural language sentence is very important in real-world question answering systems, which needs to obtain a right answer as well as a coherent natural response.