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Found 12 papers, 2 papers with code
MEE: A Novel Multilingual Event Extraction Dataset
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.
Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph
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.
Embedding Compression with Hashing for Efficient Representation Learning in Graph
When applying such type of networks on graph without node feature, one can extract simple graph-based node features (e. g., number of degrees) or learn the input node representation (i. e., embeddings) when training the network.
Online Multi-horizon Transaction Metric Estimation with Multi-modal Learning in Payment Networks
In this work, we study the problem of multivariate time series prediction for estimating transaction metrics associated with entities in the payment transaction database.
How Does Adversarial Fine-Tuning Benefit BERT?
Adversarial training (AT) is one of the most reliable methods for defending against adversarial attacks in machine learning.
How Can Self-Attention Networks Recognize Dyck-n Languages?
For $\mathcal{D}_2$, we find that SA$^-$ completely breaks down on long sequences whereas the accuracy of SA$^+$ is 58. 82$\%$.
On Adversarial Examples for Character-Level Neural Machine Translation
Evaluating on adversarial examples has become a standard procedure to measure robustness of deep learning models.
HotFlip: White-Box Adversarial Examples for Text Classification
We propose an efficient method to generate white-box adversarial examples to trick a character-level neural classifier.
A Joint Sentiment-Target-Stance Model for Stance Classification in Tweets
Classifying the stance expressed in online microblogging social media is an emerging problem in opinion mining.
