Search Results for author: Guangtao Wang

Found 19 papers, 3 papers with code

Entity and Evidence Guided Document-Level Relation Extraction

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.

Document-level Relation Extraction Language Modelling

EfficientSRFace: An Efficient Network with Super-Resolution Enhancement for Accurate Face Detection

no code implementations4 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.

Benchmarking Face Detection +1

EfficientFace: An Efficient Deep Network with Feature Enhancement for Accurate Face Detection

no code implementations23 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.

Descriptive Face Detection

SpanDrop: Simple and Effective Counterfactual Learning for Long Sequences

no code implementations3 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.

Data Augmentation

Improving Time Sensitivity for Question Answering over Temporal Knowledge Graphs

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.

Knowledge Graphs Question Answering

Open Temporal Relation Extraction for Question Answering

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).

Question Answering Reading Comprehension +1

Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification

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.

Ensemble Learning General Classification +2

Ensemble Learning Based Classification Algorithm Recommendation

no code implementations15 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.

Classification Ensemble Learning +1

Multi-hop Attention Graph Neural Network

1 code implementation29 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.

Graph Representation Learning Knowledge Graph Completion +1

Inductive Learning on Commonsense Knowledge Graph Completion

1 code implementation19 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.

Entity Embeddings Knowledge Graph Completion +2

Entity and Evidence Guided Relation Extraction for DocRED

no code implementations27 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.

Document-level Relation Extraction Language Modelling

Improving Neural Language Generation with Spectrum Control

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.

Language Modelling Machine Translation +2

Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding

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.

Knowledge Graph Embedding Link Prediction

Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple Documents

1 code implementation1 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.

Learning-To-Rank Multi-Hop Reading Comprehension +1

Improving Graph Attention Networks with Large Margin-based Constraints

no code implementations25 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.

Graph Attention Representation Learning

A Feature Subset Selection Algorithm Automatic Recommendation Method

no code implementations4 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.

feature selection General Classification +1

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