1.6k citations · 2.1k across the 14 of their papers we have counts for
15 papers
SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning
Keyu Duan, Qian Liu, Tat-Seng Chua +4
Textual graphs (TGs) are graphs whose nodes correspond to text (sentences or documents), which are widely prevalent. The representation learning of TGs involves two stages: (i) uns…
Multi-Source Test-Time Adaptation as Dueling Bandits for Extractive Question Answering
Hai Ye, Qizhe Xie, Hwee Tou Ng
In this work, we study multi-source test-time model adaptation from user feedback, where K distinct models are established for adaptation. To allow efficient adaptation, we cast th…
Automatic Model Selection with Large Language Models for Reasoning
James Xu Zhao, Yuxi Xie, Kenji Kawaguchi +2
Chain-of-Thought (CoT) and Program-Aided Language Models (PAL) represent two distinct reasoning methods, each with its own strengths. CoT employs natural language, offering flexibi…
Self-Evaluation Guided Beam Search for Reasoning
Yuxi Xie, Kenji Kawaguchi, Yiran Zhao +4
Breaking down a problem into intermediate steps has demonstrated impressive performance in Large Language Model (LLM) reasoning. However, the growth of the reasoning chain introduc…
Meta Pseudo Labels
Hieu Pham, Zihang Dai, Qizhe Xie +2
We present Meta Pseudo Labels, a semi-supervised learning method that achieves a new state-of-the-art top-1 accuracy of 90.2% on ImageNet, which is 1.6% better than the existing st…
Self-training with Noisy Student improves ImageNet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy +1
We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Noisy Student Training achieves 88.4% top-1 accuracy on I…
Unsupervised Data Augmentation for Consistency Training
Qizhe Xie, Zihang Dai, Eduard Hovy +2
Semi-supervised learning lately has shown much promise in improving deep learning models when labeled data is scarce. Common among recent approaches is the use of consistency train…
The Profiling Machine: Active Generalization over Knowledge
Filip Ilievski, Eduard Hovy, Qizhe Xie +1
The human mind is a powerful multifunctional knowledge storage and management system that performs generalization, type inference, anomaly detection, stereotyping, and other tasks.…
Fast and Simple Mixture of Softmaxes with BPE and Hybrid-LightRNN for Language Generation
Xiang Kong, Qizhe Xie, Zihang Dai +1
Mixture of Softmaxes (MoS) has been shown to be effective at addressing the expressiveness limitation of Softmax-based models. Despite the known advantage, MoS is practically seale…
From Credit Assignment to Entropy Regularization: Two New Algorithms for Neural Sequence Prediction
Zihang Dai, Qizhe Xie, Eduard Hovy
In this work, we study the credit assignment problem in reward augmented maximum likelihood (RAML) learning, and establish a theoretical equivalence between the token-level counter…
Large-scale Cloze Test Dataset Created by Teachers
Qizhe Xie, Guokun Lai, Zihang Dai +1
Cloze tests are widely adopted in language exams to evaluate students' language proficiency. In this paper, we propose the first large-scale human-created cloze test dataset CLOTH,…
An Interpretable Knowledge Transfer Model for Knowledge Base Completion
Qizhe Xie, Xuezhe Ma, Zihang Dai +1
Knowledge bases are important resources for a variety of natural language processing tasks but suffer from incompleteness. We propose a novel embedding model, \emph{ITransF}, to pe…
Controllable Invariance through Adversarial Feature Learning
Qizhe Xie, Zihang Dai, Yulun Du +2
Learning meaningful representations that maintain the content necessary for a particular task while filtering away detrimental variations is a problem of great interest in machine…
RACE: Large-scale ReAding Comprehension Dataset From Examinations
Guokun Lai, Qizhe Xie, Hanxiao Liu +2
We present RACE, a new dataset for benchmark evaluation of methods in the reading comprehension task. Collected from the English exams for middle and high school Chinese students i…
Recurrent Polynomial Network for Dialogue State Tracking
Kai Sun, Qizhe Xie, Kai Yu
Dialogue state tracking (DST) is a process to estimate the distribution of the dialogue states as a dialogue progresses. Recent studies on constrained Markov Bayesian polynomial (C…