1 code implementation • 14 May 2024 • Mengsong Wu, Tong Zhu, Han Han, Chuanyuan Tan, Xiang Zhang, Wenliang Chen
Therefore, Seal-Tools can serve as a new benchmark to evaluate the tool-calling ability of LLMs.
1 code implementation • 9 May 2024 • Dan Qiao, Yi Su, Pinzheng Wang, Jing Ye, Wenjing Xie, Yuechi Zhou, Yuyang Ding, Zecheng Tang, Jikai Wang, Yixin Ji, Yue Wang, Pei Guo, Zechen Sun, Zikang Zhang, Juntao Li, Pingfu Chao, Wenliang Chen, Guohong Fu, Guodong Zhou, Qiaoming Zhu, Min Zhang
Large Language Models (LLMs) have played an important role in many fields due to their powerful capabilities. However, their massive number of parameters leads to high deployment requirements and incurs significant inference costs, which impedes their practical applications.
2 code implementations • 12 Apr 2024 • Tianwen Tang, Tong Zhu, Haodong Liu, Yin Bai, Jia Cheng, Wenliang Chen
Zero-shot dialogue state tracking (DST) transfers knowledge to unseen domains, reducing the cost of annotating new datasets.
no code implementations • 10 Apr 2024 • Jianxiang Xiang, Zhenhua Liu, Haodong Liu, Yin Bai, Jia Cheng, Wenliang Chen
Previous studies attempted to introduce discrete or Gaussian-based continuous latent variables to address the one-to-many problem, but the diversity is limited.
no code implementations • 30 Mar 2024 • Zhenhua Liu, Tong Zhu, Jianxiang Xiang, Wenliang Chen
To evaluate the efficacy of data augmentation methods for open-domain dialogue, we designed a clustering-based metric to characterize the semantic diversity of the augmented dialogue data.
1 code implementation • 9 Nov 2023 • Tong Zhu, Junfei Ren, Zijian Yu, Mengsong Wu, Guoliang Zhang, Xiaoye Qu, Wenliang Chen, Zhefeng Wang, Baoxing Huai, Min Zhang
Sharing knowledge between information extraction tasks has always been a challenge due to the diverse data formats and task variations.
1 code implementation • 19 Sep 2023 • Juntao Li, Zecheng Tang, Yuyang Ding, Pinzheng Wang, Pei Guo, Wangjie You, Dan Qiao, Wenliang Chen, Guohong Fu, Qiaoming Zhu, Guodong Zhou, Min Zhang
This report provides the main details to pre-train an analogous model, including pre-training data processing, Bilingual Flan data collection, the empirical observations that inspire our model architecture design, training objectives of different stages, and other enhancement techniques.
no code implementations • 23 May 2023 • Chuanyuan Tan, Yuehe Chen, Wenbiao Shao, Wenliang Chen
Question answering over knowledge bases (KBQA) aims to answer factoid questions with a given knowledge base (KB).
1 code implementation • 28 Apr 2023 • Tong Zhu, Guoliang Zhang, Zechang Li, Zijian Yu, Junfei Ren, Mengsong Wu, Zhefeng Wang, Baoxing Huai, Pingfu Chao, Wenliang Chen
To address this problem, we build a large manually annotated corpus, which is the first dataset for the Catalog Extraction from Documents (CED) task.
Ranked #1 on Catalog Extraction on ChCatExt
1 code implementation • COLING 2022 • Dan Qiao, Chenchen Dai, Yuyang Ding, Juntao Li, Qiang Chen, Wenliang Chen, Min Zhang
The conventional success of textual classification relies on annotated data, and the new paradigm of pre-trained language models (PLMs) still requires a few labeled data for downstream tasks.
1 code implementation • COLING 2022 • Junjie Yu, Xing Wang, Jiangjiang Zhao, Chunjie Yang, Wenliang Chen
The approach first classifies the auto-annotated instances into two groups: confident instances and uncertain instances, according to the probabilities predicted by a teacher model.
no code implementations • 27 Apr 2022 • Yonghui Jia, Wenliang Chen
This paper presents a novel reranking method to better choose the optimal query graph, a sub-graph of knowledge graph, to retrieve the answer for an input question in Knowledge Base Question Answering (KBQA).
no code implementations • 27 Apr 2022 • Yonghui Jia, Wenliang Chen
This paper presents a novel approach based on semantic parsing to improve the performance of Knowledge Base Question Answering (KBQA).
1 code implementation • 11 Dec 2021 • Tong Zhu, Xiaoye Qu, Wenliang Chen, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan, Min Zhang
Most previous studies of document-level event extraction mainly focus on building argument chains in an autoregressive way, which achieves a certain success but is inefficient in both training and inference.
Ranked #3 on Document-level Event Extraction on ChFinAnn
no code implementations • 16 Jul 2021 • Pengju Zhang, Yonghui Jia, Muhua Zhu, Wenliang Chen, Min Zhang
Previous works for encoding questions mainly focus on the word sequences, but seldom consider the information from syntactic trees. In this paper, we propose an approach to learn syntax-based representations for KBQA.
1 code implementation • COLING 2020 • Junjie Yu, Tong Zhu, Wenliang Chen, Wei zhang, Min Zhang
In this paper, we propose an alternative approach to improve RE systems via enriching diverse expressions by relational paraphrase sentences.
1 code implementation • COLING 2020 • Tong Zhu, Haitao Wang, Junjie Yu, Xiabing Zhou, Wenliang Chen, Wei zhang, Min Zhang
The experimental results show that the ranking lists of the comparison systems on the DS-labelled test data and human-annotated test data are different.
no code implementations • 9 Mar 2020 • Xianpei Han, Zhichun Wang, Jiangtao Zhang, Qinghua Wen, Wenqi Li, Buzhou Tang, Qi. Wang, Zhifan Feng, Yang Zhang, Yajuan Lu, Haitao Wang, Wenliang Chen, Hao Shao, Yubo Chen, Kang Liu, Jun Zhao, Taifeng Wang, Kezun Zhang, Meng Wang, Yinlin Jiang, Guilin Qi, Lei Zou, Sen Hu, Minhao Zhang, Yinnian Lin
Knowledge graph models world knowledge as concepts, entities, and the relationships between them, which has been widely used in many real-world tasks.
no code implementations • 28 Nov 2019 • Zhengqiu He, Wenliang Chen, Yuyi Wang, Wei zhang, Guanchun Wang, Min Zhang
We present a novel approach to improve the performance of distant supervision relation extraction with Positive and Unlabeled (PU) Learning.
1 code implementation • 29 Aug 2019 • Haitao Wang, Zhengqiu He, Tong Zhu, Hao Shao, Wenliang Chen, Min Zhang
In this paper, we present the task definition, the description of data and the evaluation methodology used during this shared task.
1 code implementation • 30 Jul 2019 • Haitao Wang, Zhengqiu He, Jin Ma, Wenliang Chen, Min Zhang
Our data is the first dataset for inter-personal relationship extraction.
1 code implementation • COLING 2018 • Yaosheng Yang, Wenliang Chen, Zhenghua Li, Zhengqiu He, Min Zhang
A bottleneck problem with Chinese named entity recognition (NER) in new domains is the lack of annotated data.
Chinese Named Entity Recognition named-entity-recognition +5
no code implementations • 16 Jan 2018 • YaoSheng Yang, Meishan Zhang, Wenliang Chen, Wei zhang, Haofen Wang, Min Zhang
To quickly obtain new labeled data, we can choose crowdsourcing as an alternative way at lower cost in a short time.
Chinese Named Entity Recognition named-entity-recognition +2
no code implementations • 11 Jan 2018 • Zhengqiu He, Wenliang Chen, Zhenghua Li, Meishan Zhang, Wei zhang, Min Zhang
First, we encode the context of entities on a dependency tree as sentence-level entity embedding based on tree-GRU.
no code implementations • COLING 2016 • Wenliang Chen, Zhenjie Zhang, Zhenghua Li, Min Zhang
In this paper, we propose an approach to learn distributed representations of users and items from text comments for recommendation systems.