no code implementations • 8 Apr 2024 • Sirui Wang, Peiguang Li, Yunsen Xian, Hongzhi Zhang
The sequential recommendation task aims to predict the item that user is interested in according to his/her historical action sequence.
no code implementations • 27 Feb 2024 • Pei Wang, Keqing He, Yejie Wang, Xiaoshuai Song, Yutao Mou, Jingang Wang, Yunsen Xian, Xunliang Cai, Weiran Xu
Out-of-domain (OOD) intent detection aims to examine whether the user's query falls outside the predefined domain of the system, which is crucial for the proper functioning of task-oriented dialogue (TOD) systems.
1 code implementation • 20 Jan 2024 • Zhen Chen, Jingping Liu, Deqing Yang, Yanghua Xiao, Huimin Xu, ZongYu Wang, Rui Xie, Yunsen Xian
Open information extraction (OpenIE) aims to extract the schema-free triplets in the form of (\emph{subject}, \emph{predicate}, \emph{object}) from a given sentence.
no code implementations • 28 Nov 2023 • Dan Ma, Jun Xu, ZongYu Wang, Xuezhi Cao, Yunsen Xian
To facilitate research in this new task, we have constructed four datasets (Res14-EASQE, Res15-EASQE, Res16-EASQE, and Lap14-EASQE) based on the SemEval Restaurant and Laptop datasets.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +1
1 code implementation • 14 Nov 2023 • Peng Ding, Jun Kuang, Dan Ma, Xuezhi Cao, Yunsen Xian, Jiajun Chen, ShuJian Huang
Finally, we analyze the failure of LLMs defense from the perspective of prompt execution priority, and propose corresponding defense strategies.
no code implementations • 20 Oct 2023 • Pei Wang, Keqing He, Yutao Mou, Xiaoshuai Song, Yanan Wu, Jingang Wang, Yunsen Xian, Xunliang Cai, Weiran Xu
Detecting out-of-domain (OOD) intents from user queries is essential for a task-oriented dialogue system.
1 code implementation • 16 Oct 2023 • Xiaoshuai Song, Keqing He, Pei Wang, Guanting Dong, Yutao Mou, Jingang Wang, Yunsen Xian, Xunliang Cai, Weiran Xu
The tasks of out-of-domain (OOD) intent discovery and generalized intent discovery (GID) aim to extend a closed intent classifier to open-world intent sets, which is crucial to task-oriented dialogue (TOD) systems.
1 code implementation • 12 Sep 2023 • Tinghui Zhu, Jingping Liu, Jiaqing Liang, Haiyun Jiang, Yanghua Xiao, ZongYu Wang, Rui Xie, Yunsen Xian
Specifically, on the Chinese taxonomy dataset, our method significantly improves accuracy by 8. 75 %.
1 code implementation • 5 Sep 2023 • Renyu Zhu, Chengcheng Han, Yong Qian, Qiushi Sun, Xiang Li, Ming Gao, Xuezhi Cao, Yunsen Xian
To solve these issues, in this paper, we propose a novel exchanging-based multimodal fusion model MuSE for text-vision fusion based on Transformer.
1 code implementation • 28 Aug 2023 • Guanting Dong, Rumei Li, Sirui Wang, Yupeng Zhang, Yunsen Xian, Weiran Xu
Knowledge Base Question Answering (KBQA) aims to answer natural language questions with factual information such as entities and relations in KBs.
Ranked #2 on Knowledge Base Question Answering on WebQuestionsSP
1 code implementation • 25 Aug 2023 • Keheng Wang, Feiyu Duan, Sirui Wang, Peiguang Li, Yunsen Xian, Chuantao Yin, Wenge Rong, Zhang Xiong
Equipped with Chain-of-Thought (CoT), Large language models (LLMs) have shown impressive reasoning ability in various downstream tasks.
1 code implementation • 17 Jun 2023 • Weihao Zeng, Keqing He, Yejie Wang, Chen Zeng, Jingang Wang, Yunsen Xian, Weiran Xu
Pre-trained language models based on general text enable huge success in the NLP scenario.
no code implementations • 30 May 2023 • Zhuocheng Gong, Jiahao Liu, Qifan Wang, Yang Yang, Jingang Wang, Wei Wu, Yunsen Xian, Dongyan Zhao, Rui Yan
While transformer-based pre-trained language models (PLMs) have dominated a number of NLP applications, these models are heavy to deploy and expensive to use.
1 code implementation • 28 May 2023 • Yutao Mou, Xiaoshuai Song, Keqing He, Chen Zeng, Pei Wang, Jingang Wang, Yunsen Xian, Weiran Xu
Previous methods suffer from a coupling of pseudo label disambiguation and representation learning, that is, the reliability of pseudo labels relies on representation learning, and representation learning is restricted by pseudo labels in turn.
1 code implementation • 26 May 2023 • Jiduan Liu, Jiahao Liu, Qifan Wang, Jingang Wang, Wei Wu, Yunsen Xian, Dongyan Zhao, Kai Chen, Rui Yan
In this paper, we propose a novel approach, RankCSE, for unsupervised sentence representation learning, which incorporates ranking consistency and ranking distillation with contrastive learning into a unified framework.
1 code implementation • 20 May 2023 • Chen Zhang, Yang Yang, Jiahao Liu, Jingang Wang, Yunsen Xian, Benyou Wang, Dawei Song
However, when the capacity gap between the teacher and the student is large, a curse of capacity gap appears, invoking a deficiency in distilling LMs.
no code implementations • 19 May 2022 • Xiang Li, Xiaojiang Zhou, Yao Xiao, Peihao Huang, Dayao Chen, Sheng Chen, Yunsen Xian
Industrial search and recommendation systems mostly follow the classic multi-stage information retrieval paradigm: matching, pre-ranking, ranking, and re-ranking stages.