no code implementations • ACL (RepL4NLP) 2021 • Kevin Huang, Peng Qi, Guangtao Wang, Tengyu Ma, Jing Huang
In this paper, we propose a novel framework E2GRE (Entity and Evidence Guided Relation Extraction) that jointly extracts relations and the underlying evidence sentences by using large pretrained language model (LM) as input encoder.
no code implementations • 4 Jun 2023 • Guangtao Wang, Jun Li, Jie Xie, Jianhua Xu, Bo Yang
In face detection, low-resolution faces, such as numerous small faces of a human group in a crowded scene, are common in dense face prediction tasks.
no code implementations • 28 Apr 2023 • Jie Liu, Mengting He, Guangtao Wang, Nguyen Quoc Viet Hung, Xuequn Shang, Hongzhi Yin
minority classes to balance the label and topology distribution.
no code implementations • 23 Feb 2023 • Guangtao Wang, Jun Li, Zhijian Wu, Jianhua Xu, Jifeng Shen, Wankou Yang
Besides, this is conducive to estimating the locations of faces and enhancing the descriptive power of face features.
no code implementations • 3 Aug 2022 • Peng Qi, Guangtao Wang, Jing Huang
Distilling supervision signal from a long sequence to make predictions is a challenging task in machine learning, especially when not all elements in the input sequence contribute equally to the desired output.
no code implementations • ACL 2022 • Chao Shang, Guangtao Wang, Peng Qi, Jing Huang
These questions often involve three time-related challenges that previous work fail to adequately address: 1) questions often do not specify exact timestamps of interest (e. g., "Obama" instead of 2000); 2) subtle lexical differences in time relations (e. g., "before" vs "after"); 3) off-the-shelf temporal KG embeddings that previous work builds on ignore the temporal order of timestamps, which is crucial for answering temporal-order related questions.
Ranked #2 on
Question Answering
on CronQuestions
no code implementations • AKBC 2021 • Chao Shang, Peng Qi, Guangtao Wang, Jing Huang, Youzheng Wu, BoWen Zhou
Understanding the temporal relations among events in text is a critical aspect of reading comprehension, which can be evaluated in the form of temporal question answering (TQA).
no code implementations • NAACL 2021 • Xiaochen Hou, Peng Qi, Guangtao Wang, Rex Ying, Jing Huang, Xiaodong He, BoWen Zhou
Recent work on aspect-level sentiment classification has demonstrated the efficacy of incorporating syntactic structures such as dependency trees with graph neural networks(GNN), but these approaches are usually vulnerable to parsing errors.
no code implementations • 15 Jan 2021 • Guangtao Wang, Qinbao Song, Xiaoyan Zhu
Recommending appropriate algorithms to a classification problem is one of the most challenging issues in the field of data mining.
1 code implementation • 29 Sep 2020 • Guangtao Wang, Rex Ying, Jing Huang, Jure Leskovec
Currently, at every layer, attention is computed between connected pairs of nodes and depends solely on the representation of the two nodes.
1 code implementation • 19 Sep 2020 • Bin Wang, Guangtao Wang, Jing Huang, Jiaxuan You, Jure Leskovec, C. -C. Jay Kuo
Here, we propose to study the inductive learning setting for CKG completion where unseen entities may present at test time.
no code implementations • 27 Aug 2020 • Kevin Huang, Guangtao Wang, Tengyu Ma, Jing Huang
Document-level relation extraction is a challenging task which requires reasoning over multiple sentences in order to predict relations in a document.
Ranked #14 on
Relation Extraction
on DocRED
no code implementations • ICLR 2020 • Lingxiao Wang, Jing Huang, Kevin Huang, Ziniu Hu, Guangtao Wang, Quanquan Gu
Recent Transformer-based models such as Transformer-XL and BERT have achieved huge success on various natural language processing tasks.
no code implementations • ACL 2020 • Yun Tang, Jing Huang, Guangtao Wang, Xiaodong He, Bo-Wen Zhou
Translational distance-based knowledge graph embedding has shown progressive improvements on the link prediction task, from TransE to the latest state-of-the-art RotatE.
Ranked #19 on
Link Prediction
on FB15k-237
1 code implementation • 1 Nov 2019 • Ming Tu, Kevin Huang, Guangtao Wang, Jing Huang, Xiaodong He, Bo-Wen Zhou
Interpretable multi-hop reading comprehension (RC) over multiple documents is a challenging problem because it demands reasoning over multiple information sources and explaining the answer prediction by providing supporting evidences.
no code implementations • 25 Oct 2019 • Guangtao Wang, Rex Ying, Jing Huang, Jure Leskovec
Graph Attention Networks (GATs) are the state-of-the-art neural architecture for representation learning with graphs.
no code implementations • NAACL (TextGraphs) 2021 • Xiaochen Hou, Jing Huang, Guangtao Wang, Xiaodong He, BoWen Zhou
Aspect-level sentiment classification aims to identify the sentiment polarity towards a specific aspect term in a sentence.
no code implementations • ACL 2019 • Ming Tu, Guangtao Wang, Jing Huang, Yun Tang, Xiaodong He, Bo-Wen Zhou
We introduce a heterogeneous graph with different types of nodes and edges, which is named as Heterogeneous Document-Entity (HDE) graph.
no code implementations • 4 Feb 2014 • Guangtao Wang, Qinbao Song, Heli Sun, Xueying Zhang, Baowen Xu, Yuming Zhou
The performance of the candidate FSS algorithms is evaluated by a multi-criteria metric that takes into account not only the classification accuracy over the selected features, but also the runtime of feature selection and the number of selected features.