no code implementations • COLING 2022 • Yang Sun, Liangqing Wu, Shuangyong Song, Xiaoguang Yu, Xiaodong He, Guohong Fu
In this work, we investigate the problem of satisfaction states tracking and its effects on CSP in E-commerce service chatbots.
no code implementations • 11 Jul 2024 • Zhenhe Wu, Zhongqiu Li, Jie Zhang, Mengxiang Li, Yu Zhao, Ruiyu Fang, Zhongjiang He, Xuelong Li, Zhoujun Li, Shuangyong Song
Large language models (LLMs) with in-context learning have significantly improved the performance of text-to-SQL task.
no code implementations • 3 Jul 2024 • Xiang Li, Yiqun Yao, Xin Jiang, Xuezhi Fang, Chao Wang, Xinzhang Liu, Zihan Wang, Yu Zhao, Xin Wang, Yuyao Huang, Shuangyong Song, Yongxiang Li, Zheng Zhang, Bo Zhao, Aixin Sun, Yequan Wang, Zhongjiang He, Zhongyuan Wang, Xuelong Li, Tiejun Huang
Large Language Models (LLMs) represent a significant stride toward Artificial General Intelligence.
1 code implementation • 14 Jun 2024 • Yuxin Dong, Tieliang Gong, Hong Chen, Shuangyong Song, Weizhan Zhang, Chen Li
Domain generalization aims to learn invariance across multiple training domains, thereby enhancing generalization against out-of-distribution data.
no code implementations • 25 Apr 2024 • Xiang Li, Yiqun Yao, Xin Jiang, Xuezhi Fang, Chao Wang, Xinzhang Liu, Zihan Wang, Yu Zhao, Xin Wang, Yuyao Huang, Shuangyong Song, Yongxiang Li, Zheng Zhang, Bo Zhao, Aixin Sun, Yequan Wang, Zhongjiang He, Zhongyuan Wang, Xuelong Li, Tiejun Huang
Large language models (LLMs) have showcased profound capabilities in language understanding and generation, facilitating a wide array of applications.
no code implementations • 16 Mar 2024 • Zihan Wang, Jiayu Xiao, Mengxiang Li, Zhongjiang He, Yongxiang Li, Chao Wang, Shuangyong Song
In our dynamic world where data arrives in a continuous stream, continual learning enables us to incrementally add new tasks/domains without the need to retrain from scratch.
no code implementations • 8 Jan 2024 • Zhongjiang He, Zihan Wang, Xinzhang Liu, Shixuan Liu, Yitong Yao, Yuyao Huang, Xuelong Li, Yongxiang Li, Zhonghao Che, Zhaoxi Zhang, Yan Wang, Xin Wang, Luwen Pu, Huinan Xu, Ruiyu Fang, Yu Zhao, Jie Zhang, Xiaomeng Huang, Zhilong Lu, Jiaxin Peng, Wenjun Zheng, Shiquan Wang, Bingkai Yang, Xuewei he, Zhuoru Jiang, Qiyi Xie, Yanhan Zhang, Zhongqiu Li, Lingling Shi, Weiwei Fu, Yin Zhang, Zilu Huang, Sishi Xiong, Yuxiang Zhang, Chao Wang, Shuangyong Song
Subsequently, the model undergoes fine-tuning to align with human preferences, following a detailed methodology that we describe.
no code implementations • 20 Jun 2023 • Kai Ouyang, Xianghong Xu, Miaoxin Chen, Zuotong Xie, Hai-Tao Zheng, Shuangyong Song, Yu Zhao
Session-based Recommendation (SR) aims to predict users' next click based on their behavior within a short period, which is crucial for online platforms.
no code implementations • 29 Jul 2021 • Shuangyong Song
Knowledge graphs (KGs) have been widely used for question answering (QA) applications, especially the entity based QA.
no code implementations • NAACL 2021 • Shuangyong Song, Chao Wang, Haiqing Chen, Huan Chen
E-commerce has grown substantially over the last several years, and chatbots for intelligent customer service are concurrently drawing attention.
no code implementations • 4 Mar 2021 • Shuangyong Song, Kexin Wang, Chao Wang, Haiqing Chen, Huan Chen
In response generation task, proper sentimental expressions can obviously improve the human-like level of the responses.
no code implementations • 23 Apr 2020 • Shuangyong Song, Chao Wang
Automatic question-answering (QA) systems have boomed during last few years, and commonly used techniques can be roughly categorized into Information Retrieval (IR)-based and generation-based.
no code implementations • 13 Apr 2020 • Shuangyong Song, Chao Wang, Qianqian Xie, Xinxing Zu, Huan Chen, Haiqing Chen
In this paper, we propose the conversational query rewriting model - MLR, which is a Multi-task model on sequence Labeling and query Rewriting.
no code implementations • 23 Nov 2017 • Jianfei Yu, Minghui Qiu, Jing Jiang, Jun Huang, Shuangyong Song, Wei Chu, Haiqing Chen
In this paper, we study transfer learning for the PI and NLI problems, aiming to propose a general framework, which can effectively and efficiently adapt the shared knowledge learned from a resource-rich source domain to a resource- poor target domain.
no code implementations • 5 May 2015 • Shuangyong Song, Yao Meng, Zhongguang Zheng, Jun Sun
Our FRDC_QA team participated in the QA-Lab English subtask of the NTCIR-11.
no code implementations • 30 Apr 2015 • Shuangyong Song, Yao Meng
In this paper, we propose a Concept-level Emotion Cause Model (CECM), instead of the mere word-level models, to discover causes of microblogging users' diversified emotions on specific hot event.