3.4k citations · 4.6k across the 38 of their papers we have counts for
13 papers · 1 filter
Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data
Lingkai Kong, Haoming Jiang, Yuchen Zhuang +3
Fine-tuned pre-trained language models can suffer from severe miscalibration for both in-distribution and out-of-distribution (OOD) data due to over-parameterization. To mitigate t…
Text Classification Using Label Names Only: A Language Model Self-Training Approach
Yu Meng, Yunyi Zhang, Jiaxin Huang +4
Current text classification methods typically require a good number of human-labeled documents as training data, which can be costly and difficult to obtain in real applications. H…
Denoising Multi-Source Weak Supervision for Neural Text Classification
Wendi Ren, Yinghao Li, Hanting Su +3
We study the problem of learning neural text classifiers without using any labeled data, but only easy-to-provide rules as multiple weak supervision sources. This problem is challe…
SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup
Rongzhi Zhang, Yue Yu, Chao Zhang
Active learning is an important technique for low-resource sequence labeling tasks. However, current active sequence labeling methods use the queried samples alone in each iteratio…
A Spherical Hidden Markov Model for Semantics-Rich Human Mobility Modeling
Wanzheng Zhu, Chao Zhang, Shuochao Yao +2
We study the problem of modeling human mobility from semantic trace data, wherein each GPS record in a trace is associated with a text message that describes the user's activity. E…
Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach
Yue Yu, Simiao Zuo, Haoming Jiang +3
Fine-tuned pre-trained language models (LMs) have achieved enormous success in many natural language processing (NLP) tasks, but they still require excessive labeled data in the fi…
SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization
Yue Yu, Kexin Huang, Chao Zhang +3
Thanks to the increasing availability of drug-drug interactions (DDI) datasets and large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using machine learning…
Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization
Qi Zhu, Carl Yang, Yidan Xu +3
Graph neural networks (GNNs) have achieved superior performance in various applications, but training dedicated GNNs can be costly for large-scale graphs. Some recent work started…
SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates
Lingkai Kong, Jimeng Sun, Chao Zhang
Uncertainty quantification is a fundamental yet unsolved problem for deep learning. The Bayesian framework provides a principled way of uncertainty estimation but is often not scal…
Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding
Yu Meng, Yunyi Zhang, Jiaxin Huang +3
Mining a set of meaningful topics organized into a hierarchy is intuitively appealing since topic correlations are ubiquitous in massive text corpora. To account for potential hier…
BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision
Chen Liang, Yue Yu, Haoming Jiang +4
We study the open-domain named entity recognition (NER) problem under distant supervision. The distant supervision, though does not require large amounts of manual annotations, yie…
Partially-Typed NER Datasets Integration: Connecting Practice to Theory
Shi Zhi, Liyuan Liu, Yu Zhang +4
While typical named entity recognition (NER) models require the training set to be annotated with all target types, each available datasets may only cover a part of them. Instead o…
paper2repo: GitHub Repository Recommendation for Academic Papers
Huajie Shao, Dachun Sun, Jiahao Wu +7
GitHub has become a popular social application platform, where a large number of users post their open source projects. In particular, an increasing number of researchers release r…