no code implementations • EMNLP 2021 • Yong Guan, Shaoru Guo, Ru Li, XiaoLi Li, Hu Zhang
Recently graph-based methods have been adopted for Abstractive Text Summarization.
no code implementations • EMNLP 2021 • Yong Guan, Shaoru Guo, Ru Li, XiaoLi Li, Hongye Tan
In this paper, we propose a novel Frame Semantic-Enhanced Sentence Modeling for Extractive Summarization, which leverages Frame semantics to model sentences from both intra-sentence level and inter-sentence level, facilitating the text summarization task.
no code implementations • 13 Aug 2024 • Yong Guan, Hao Peng, Xiaozhi Wang, Lei Hou, Juanzi Li
For question construction, we pose questions from seven perspectives, including location, time, event development, event outcome, event impact, event response, and other, to facilitate an in-depth analysis and understanding of the comprehensive evolution of events.
no code implementations • 11 May 2024 • Yong Guan, Xiaozhi Wang, Lei Hou, Juanzi Li, Jeff Pan, Jiaoyan Chen, Freddy Lecue
Existing work mainly focuses on directly modeling the entire document, which cannot effectively handle long-range dependencies and information redundancy.
no code implementations • 11 May 2024 • Yong Guan, Dingxiao Liu, Jinchen Ma, Hao Peng, Xiaozhi Wang, Lei Hou, Ru Li
Inspired by this, we propose Event GDR, an event-centric generative document retrieval model, integrating event knowledge into this task.
no code implementations • 29 Jan 2024 • Yong Guan, Freddy Lecue, Jiaoyan Chen, Ru Li, Jeff Z. Pan
Specifically, for concept completeness, we present core concepts of a scene based on knowledge graph, ConceptNet, to gauge the completeness of concepts.
1 code implementation • 15 Nov 2023 • Xiaozhi Wang, Hao Peng, Yong Guan, Kaisheng Zeng, Jianhui Chen, Lei Hou, Xu Han, Yankai Lin, Zhiyuan Liu, Ruobing Xie, Jie zhou, Juanzi Li
Understanding events in texts is a core objective of natural language understanding, which requires detecting event occurrences, extracting event arguments, and analyzing inter-event relationships.
1 code implementation • 27 Oct 2023 • Dong Qin, George Amariucai, Daji Qiao, Yong Guan, Shen Fu
While avoiding the artifacts problem, this new category suffers from the Encoding Prediction in the Explanation (EPITE) problem, in which the predictor's decisions rely not on the features, but on the masks that selects those features.
1 code implementation • 15 Jun 2023 • Jifan Yu, Xiaozhi Wang, Shangqing Tu, Shulin Cao, Daniel Zhang-li, Xin Lv, Hao Peng, Zijun Yao, Xiaohan Zhang, Hanming Li, Chunyang Li, Zheyuan Zhang, Yushi Bai, Yantao Liu, Amy Xin, Nianyi Lin, Kaifeng Yun, Linlu Gong, Jianhui Chen, Zhili Wu, Yunjia Qi, Weikai Li, Yong Guan, Kaisheng Zeng, Ji Qi, Hailong Jin, Jinxin Liu, Yu Gu, Yuan YAO, Ning Ding, Lei Hou, Zhiyuan Liu, Bin Xu, Jie Tang, Juanzi Li
The unprecedented performance of large language models (LLMs) necessitates improvements in evaluations.
no code implementations • COLING 2020 • Shaoru Guo, Yong Guan, Ru Li, XiaoLi Li, Hongye Tan
Machine reading comprehension (MRC) is one of the most critical yet challenging tasks in natural language understanding(NLU), where both syntax and semantics information of text are essential components for text understanding.
Machine Reading Comprehension
Natural Language Understanding
no code implementations • ACL 2020 • Shaoru Guo, Ru Li, Hongye Tan, Xiao-Li Li, Yong Guan, Hongyan Zhao, Yueping Zhang
Sentence representation (SR) is the most crucial and challenging task in Machine Reading Comprehension (MRC).
no code implementations • 8 Jan 2020 • Yixing Huang, Shengxiang Wang, Yong Guan, Andreas Maier
Particularly, the U-Net, the state-of-the-art neural network in biomedical imaging, is trained from synthetic ellipsoid data and multi-category data to reduce artifacts in filtered back-projection (FBP) reconstruction images.
no code implementations • 1 Oct 2018 • Zhenzhou Shao, Hongfa Zhao, Jiexin Xie, Ying Qu, Yong Guan, Jindong Tan
To improve the efficiency of surgical trajectory segmentation for robot learning in robot-assisted minimally invasive surgery, this paper presents a fast unsupervised method using video and kinematic data, followed by a promoting procedure to address the over-segmentation issue.