no code implementations • Findings (EMNLP) 2021 • Haoyu Wang, Fenglong Ma, Yaqing Wang, Jing Gao
We propose to mine outline knowledge of concepts related to given sentences from Wikipedia via BM25 model.
no code implementations • 24 May 2022 • Yaqing Wang, Subhabrata Mukherjee, Xiaodong Liu, Jing Gao, Ahmed Hassan Awadallah, Jianfeng Gao
(ii) We propose a simple merging mechanism to average the weights of multiple adapter components to collapse to a single adapter in each Transformer layer, thereby, keeping the overall parameters also the same but with significant performance improvement.
no code implementations • 6 May 2022 • Quanming Yao, Yaqing Wang, Bo Han, James Kwok
While the optimization problem is nonconvex and nonsmooth, we show that its critical points still have good statistical performance on the tensor completion problem.
1 code implementation • EMNLP 2021 • Yaqing Wang, Song Wang, Quanming Yao, Dejing Dou
Short text classification is a fundamental task in natural language processing.
1 code implementation • 12 Oct 2021 • Yaqing Wang, Subhabrata Mukherjee, Xiaodong Liu, Jing Gao, Ahmed Hassan Awadallah, Jianfeng Gao
The first is the use of self-training to leverage large amounts of unlabeled data for prompt-based FN in few-shot settings.
no code implementations • 6 Oct 2021 • Haoran Liu, Haoyi Xiong, Yaqing Wang, Haozhe An, Dongrui Wu, Dejing Dou
Specifically, we design a new metric $\mathcal{P}$-vector to represent the principal subspace of deep features learned in a DNN, and propose to measure angles between the principal subspaces using $\mathcal{P}$-vectors.
no code implementations • 12 Sep 2021 • Liwei Che, Zewei Long, Jiaqi Wang, Yaqing Wang, Houping Xiao, Fenglong Ma
In particular, we propose to use three networks and a dynamic quality control mechanism to generate high-quality pseudo labels for unlabeled data, which are added to the training set.
no code implementations • Findings (EMNLP) 2021 • Yaqing Wang, Haoda Chu, Chao Zhang, Jing Gao
In this work, we study the problem of named entity recognition (NER) in a low resource scenario, focusing on few-shot and zero-shot settings.
no code implementations • 9 Sep 2021 • Zewei Long, Jiaqi Wang, Yaqing Wang, Houping Xiao, Fenglong Ma
Most existing FedSSL methods focus on the classical scenario, i. e, the labeled and unlabeled data are stored at the client side.
no code implementations • NeurIPS 2021 • Yaqing Wang, Abulikemu Abuduweili, Quanming Yao, Dejing Dou
the target property, such that the limited labels can be effectively propagated among similar molecules.
no code implementations • 22 Jun 2021 • Yaqing Wang, Fenglong Ma, Haoyu Wang, Kishlay Jha, Jing Gao
The experimental results show our proposed MetaFEND model can detect fake news on never-seen events effectively and outperform the state-of-the-art methods.
no code implementations • 1 Jan 2021 • Haoran Liu, Haoyi Xiong, Yaqing Wang, Haozhe An, Dongrui Wu, Dejing Dou
While deep learning is effective to learn features/representations from data, the distributions of samples in feature spaces learned by various architectures for different training tasks (e. g., latent layers of AEs and feature vectors in CNN classifiers) have not been well-studied or compared.
no code implementations • 1 Jan 2021 • Yaqing Wang, Subhabrata Mukherjee, Haoda Chu, Yuancheng Tu, Ming Wu, Jing Gao, Ahmed Hassan Awadallah
Neural sequence labeling is an important technique employed for many Natural Language Processing (NLP) tasks, such as Named Entity Recognition (NER), slot tagging for dialog systems and semantic parsing.
no code implementations • 6 Dec 2020 • Zewei Long, Liwei Che, Yaqing Wang, Muchao Ye, Junyu Luo, Jinze Wu, Houping Xiao, Fenglong Ma
In this paper, we focus on designing a general framework FedSiam to tackle different scenarios of federated semi-supervised learning, including four settings in the labels-at-client scenario and two setting in the labels-at-server scenario.
no code implementations • 4 Dec 2020 • Junyu Luo, Zifei Zheng, Hanzhong Ye, Muchao Ye, Yaqing Wang, Quanzeng You, Cao Xiao, Fenglong Ma
In this paper, we introduce MedLane -- a new human-annotated Medical Language translation dataset, to align professional medical sentences with layperson-understandable expressions.
no code implementations • 7 Oct 2020 • Yaqing Wang, Subhabrata Mukherjee, Haoda Chu, Yuancheng Tu, Ming Wu, Jing Gao, Ahmed Hassan Awadallah
While self-training serves as an effective mechanism to learn from large amounts of unlabeled data -- meta-learning helps in adaptive sample re-weighting to mitigate error propagation from noisy pseudo-labels.
no code implementations • 16 Aug 2020 • Yaqing Wang, Fenglong Ma, Jing Gao
To tackle this challenging task, we propose a cross-graph representation learning framework, i. e., CrossVal, which can leverage an external KG to validate the facts in the target KG efficiently.
no code implementations • 14 Aug 2020 • Yaqing Wang, Quanming Yao, James T. Kwok
Extensive low-rank matrix completion experiments on a number of synthetic and real-world data sets show that the proposed method obtains state-of-the-art recovery performance while being the fastest in comparison to existing low-rank matrix learning methods.
no code implementations • 24 Jun 2020 • Xin Luna Dong, Xiang He, Andrey Kan, Xi-An Li, Yan Liang, Jun Ma, Yifan Ethan Xu, Chenwei Zhang, Tong Zhao, Gabriel Blanco Saldana, Saurabh Deshpande, Alexandre Michetti Manduca, Jay Ren, Surender Pal Singh, Fan Xiao, Haw-Shiuan Chang, Giannis Karamanolakis, Yuning Mao, Yaqing Wang, Christos Faloutsos, Andrew McCallum, Jiawei Han
Can one build a knowledge graph (KG) for all products in the world?
no code implementations • 15 Jun 2020 • Yaqing Wang, Yifan Ethan Xu, Xi-An Li, Xin Luna Dong, Jing Gao
(1) We formalize the problem of validating the textual attribute values of products from a variety of categories as a natural language inference task in the few-shot learning setting, and propose a meta-learning latent variable model to jointly process the signals obtained from product profiles and textual attribute values.
no code implementations • 21 Apr 2020 • Alexander Hanbo Li, Yaqing Wang, Changyou Chen, Jing Gao
Effective inference for a generative adversarial model remains an important and challenging problem.
1 code implementation • 28 Dec 2019 • Yaqing Wang, Weifeng Yang, Fenglong Ma, Jin Xu, Bin Zhong, Qiang Deng, Jing Gao
In order to tackle this challenge, we propose a reinforced weakly-supervised fake news detection framework, i. e., WeFEND, which can leverage users' reports as weak supervision to enlarge the amount of training data for fake news detection.
4 code implementations • 10 Apr 2019 • Yaqing Wang, Quanming Yao, James Kwok, Lionel M. Ni
Machine learning has been highly successful in data-intensive applications but is often hampered when the data set is small.
no code implementations • 8 Mar 2019 • Yaqing Wang, James T. Kwok, Lionel M. Ni
However, existing CSC methods can only model noises from Gaussian distribution, which is restrictive and unrealistic.
no code implementations • 27 Sep 2018 • Hanbo Li, Yaqing Wang, Changyou Chen, Jing Gao
We propose a novel approach, Adversarial Inference by Matching priors and conditionals (AIM), which explicitly matches prior and conditional distributions in both data and code spaces, and puts a direct constraint on the dependency structure of the generative model.
1 code implementation • Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2018 • Yaqing Wang, Fenglong Ma, Zhiwei Jin, Ye Yuan, Guangxu Xun, Kishlay Jha, Lu Su, Jing Gao
One of the unique challenges for fake news detection on social media is how to identify fake news on newly emerged events.
no code implementations • ICML 2018 • Yaqing Wang, Quanming Yao, James T. Kwok, Lionel M. Ni
Convolutional sparse coding (CSC) has been popularly used for the learning of shift-invariant dictionaries in image and signal processing.
no code implementations • 21 Jun 2017 • Yaqing Wang, Quanming Yao, James T. Kwok, Lionel M. Ni
Convolutional sparse coding (CSC) improves sparse coding by learning a shift-invariant dictionary from the data.