Search Results for author: Yijun Wang

Found 13 papers, 6 papers with code

Pre-training Entity Relation Encoder with Intra-span and Inter-span Information

no code implementations EMNLP 2020 Yijun Wang, Changzhi Sun, Yuanbin Wu, Junchi Yan, Peng Gao, Guotong Xie

In particular, a span encoder is trained to recover a random shuffling of tokens in a span, and a span pair encoder is trained to predict positive pairs that are from the same sentences and negative pairs that are from different sentences using contrastive loss.

Relation Extraction

汉语学习者依存句法树库构建(Construction of a Treebank of Learner Chinese)

no code implementations CCL 2020 Jialu Shi, Xinyu Luo, Liner Yang, Dan Xiao, Zhengsheng Hu, Yijun Wang, Jiaxin Yuan, Yu Jingsi, Erhong Yang

汉语学习者依存句法树库为非母语者语料提供依存句法分析, 可以支持第二语言教学与研究, 也对面向第二语言的句法分析、语法改错等相关研究具有重要意义。然而, 现有的汉语学习者依存句法树库数量较少, 且在标注方面仍存在一些问题。为此, 本文改进依存句法标注规范, 搭建在线标注平台, 并开展汉语学习者依存句法标注。本文重点介绍了数据选取、标注流程等问题, 并对标注结果进行质量分析, 探索二语偏误对标注质量与句法分析的影响。

YACLC: A Chinese Learner Corpus with Multidimensional Annotation

no code implementations30 Dec 2021 Yingying Wang, Cunliang Kong, Liner Yang, Yijun Wang, Xiaorong Lu, Renfen Hu, Shan He, Zhenghao Liu, Yun Chen, Erhong Yang, Maosong Sun

This resource is of great relevance for second language acquisition research, foreign-language teaching, and automatic grammatical error correction.

Grammatical Error Correction Language Acquisition

ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction

1 code implementation EACL 2021 Yijun Wang, Changzhi Sun, Yuanbin Wu, Hao Zhou, Lei LI, Junchi Yan

Current state-of-the-art systems for joint entity relation extraction (Luan et al., 2019; Wad-den et al., 2019) usually adopt the multi-task learning framework.

Coreference Resolution Entity Typing +3

Gravitational Wave Detection with Photometric Surveys

1 code implementation5 Oct 2020 Yijun Wang, Kris Pardo, Tzu-Ching Chang, Olivier Doré

While the detection threshold assuming the currently expected performance proves too high for detecting individual GWs in light of the expected supermassive black hole binary population distribution, we show that binaries with chirp mass $M_c>10^{8. 3}~M_\odot$ out to 100 Mpc can be detected if the telescope is able to achieve an astrometric accuracy of 0. 11 mas.

General Relativity and Quantum Cosmology Cosmology and Nongalactic Astrophysics

MMEA: Entity Alignment for Multi-Modal Knowledge Graphs

1 code implementation20 Aug 2020 Liyi Chen, Zhi Li, Yijun Wang, Tong Xu, Zhefeng Wang, Enhong Chen

To that end, in this paper, we propose a novel solution called Multi-Modal Entity Alignment (MMEA) to address the problem of entity alignment in a multi-modal view.

Entity Alignment Knowledge Graphs +1

Learning to Transfer: Unsupervised Meta Domain Translation

1 code implementation1 Jun 2019 Jianxin Lin, Yijun Wang, Tianyu He, Zhibo Chen

Unsupervised domain translation has recently achieved impressive performance with Generative Adversarial Network (GAN) and sufficient (unpaired) training data.

Meta-Learning Translation

Image-to-Image Translation with Multi-Path Consistency Regularization

no code implementations29 May 2019 Jianxin Lin, Yingce Xia, Yijun Wang, Tao Qin, Zhibo Chen

In this work, we introduce a new kind of loss, multi-path consistency loss, which evaluates the differences between direct translation $\mathcal{D}_s\to\mathcal{D}_t$ and indirect translation $\mathcal{D}_s\to\mathcal{D}_a\to\mathcal{D}_t$ with $\mathcal{D}_a$ as an auxiliary domain, to regularize training.

Face to Face Translation Image-to-Image Translation +1

Ensemble Pruning based on Objection Maximization with a General Distributed Framework

1 code implementation13 Jun 2018 Yijun Bian, Yijun Wang, Yaqiang Yao, Huanhuan Chen

Ensemble pruning, selecting a subset of individual learners from an original ensemble, alleviates the deficiencies of ensemble learning on the cost of time and space.

Ensemble Learning Ensemble Pruning

Classifying Single-Trial EEG during Motor Imagery with a Small Training Set

no code implementations14 Jun 2013 Yijun Wang

Before the operation of a motor imagery based brain-computer interface (BCI) adopting machine learning techniques, a cumbersome training procedure is unavoidable.


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