169 citations · 255 across the 7 of their papers we have counts for
7 papers
MEE: A Novel Multilingual Event Extraction Dataset
Amir Pouran Ben Veyseh, Javid Ebrahimi, Franck Dernoncourt +1
Event Extraction (EE) is one of the fundamental tasks in Information Extraction (IE) that aims to recognize event mentions and their arguments (i.e., participants) from text. Due t…
Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph
Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng +6
Graph neural networks (GNNs) are deep learning models designed specifically for graph data, and they typically rely on node features as the input to the first layer. When applying…
How Does Adversarial Fine-Tuning Benefit BERT?
Javid Ebrahimi, Hao Yang, Wei Zhang
Adversarial training (AT) is one of the most reliable methods for defending against adversarial attacks in machine learning. Variants of this method have been used as regularizatio…
Online Multi-horizon Transaction Metric Estimation with Multi-modal Learning in Payment Networks
Chin-Chia Michael Yeh, Zhongfang Zhuang, Junpeng Wang +5
Predicting metrics associated with entities' transnational behavior within payment processing networks is essential for system monitoring. Multivariate time series, aggregated from…
How Can Self-Attention Networks Recognize Dyck-n Languages?
Javid Ebrahimi, Dhruv Gelda, Wei Zhang
We focus on the recognition of Dyck-n () languages with self-attention (SA) networks, which has been deemed to be a difficult task for these networks. We compare the…
On Adversarial Examples for Character-Level Neural Machine Translation
Javid Ebrahimi, Daniel Lowd, Dejing Dou
Evaluating on adversarial examples has become a standard procedure to measure robustness of deep learning models. Due to the difficulty of creating white-box adversarial examples f…