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20182023
most citedDistantly Supervised Named Entity Recognition using Positive-Unlabeled Learning

12 citations · 16 across the 5 of their papers we have counts for

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6 papers · 1 filter

cs.CL2023

On the Universal Adversarial Perturbations for Efficient Data-free Adversarial Detection

Songyang Gao, Shihan Dou, Qi Zhang +3

Detecting adversarial samples that are carefully crafted to fool the model is a critical step to socially-secure applications. However, existing adversarial detection methods requi…

cs.CL2023

CausalAPM: Generalizable Literal Disentanglement for NLU Debiasing

Songyang Gao, Shihan Dou, Junjie Shan +2

Dataset bias, i.e., the over-reliance on dataset-specific literal heuristics, is getting increasing attention for its detrimental effect on the generalization ability of NLU models…

cs.CL2022★ 2 cited

Kernel-Whitening: Overcome Dataset Bias with Isotropic Sentence Embedding

Songyang Gao, Shihan Dou, Qi Zhang +1

Dataset bias has attracted increasing attention recently for its detrimental effect on the generalization ability of fine-tuned models. The current mainstream solution is designing…

cs.CL2019★ 12 cited

Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning

Minlong Peng, Xiaoyu Xing, Qi Zhang +2

In this work, we explore the way to perform named entity recognition (NER) using only unlabeled data and named entity dictionaries. To this end, we formulate the task as a positive…

cs.CL2019★ 2 cited

Learning Task-specific Representation for Novel Words in Sequence Labeling

Minlong Peng, Qi Zhang, Xiaoyu Xing +3

Word representation is a key component in neural-network-based sequence labeling systems. However, representations of unseen or rare words trained on the end task are usually poor…

cs.CL2018

Long Short-Term Memory with Dynamic Skip Connections

Tao Gui, Qi Zhang, Lujun Zhao +4

In recent years, long short-term memory (LSTM) has been successfully used to model sequential data of variable length. However, LSTM can still experience difficulty in capturing lo…