Conference

Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive Learning
To overcome the data sparsity issue in short text topic modeling, existing methods commonly rely on data augmentation or the data …
Recipe for a General, Powerful, Scalable Graph Transformer
We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art …
Textual Manifold-based Defense Against Natural Language Adversarial Examples
Despite the recent success of large pretrained language models in NLP, they are susceptible to adversarial examples. Concurrently, …
How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?
The fine-tuning of pre-trained language models has a great success in many NLP fields. Yet, it is strikingly vulnerable to adversarial …
Improving Neural Cross-Lingual Summarization via Employing Optimal Transport Distance for Knowledge Distillation
Current state-of-the-art cross-lingual summarization models employ multi-task learning paradigm, which works on a shared vocabulary …
Contrastive Learning for Neural Topic Model
Recent empirical studies show that adversarial topic models (ATM) can successfully capture semantic patterns of the document by …
Graph Neural Networks with Learnable Structural and Positional Representations
Graph neural networks (GNNs) have become the standard learning architectures for graphs. GNNs have been applied to numerous domains …
Towards Robustness Against Natural Language Word Substitutions
Robustness against word substitutions has a well-defined and widely acceptable form, i.e., using semantically similar words as …
Beyond Fully-Connected Layers with Quaternions:Parameterization of Hypercomplex Multiplications with 1/n Parameters
Recent works have demonstrated reasonable success of representation learning in hypercomplex space. Specifically, …
Deep Learning to Estimate RECIST in Patients with NSCLC Treated with PD-1 Blockade
Real-world evidence (RWE), conclusions derived from analysis of patients not treated in clinical trials, is increasingly recognized as …