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Found 22 papers, 10 papers with code
Authorship Attribution for Neural Text Generation
In recent years, the task of generating realistic short and long texts have made tremendous advancements.
Detecting False Claims in Low-Resource Regions: A Case Study of Caribbean Islands
The COVID-19 pandemic has created threats to global health control.
Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense
We find that adversarial texts generated by ANTHRO achieve the best trade-off between (1) attack success rate, (2) semantic preservation of the original text, and (3) stealthiness--i. e. indistinguishable from human writings hence harder to be flagged as suspicious.
Do Language Models Plagiarize?
Past literature has illustrated that language models do not fully understand the context and sensitivity of text and can sometimes memorize phrases or sentences present in their training sets.
JAMES: Job Title Mapping with Multi-Aspect Embeddings and Reasoning
One of the most essential tasks needed for various downstream tasks in career analytics (e. g., career trajectory analysis, job mobility prediction, and job recommendation) is Job Title Mapping (JTM), where the goal is to map user-created (noisy and non-standard) job titles to predefined and standard job titles.
Socialbots on Fire: Modeling Adversarial Behaviors of Socialbots via Multi-Agent Hierarchical Reinforcement Learning
To this question, we successfully demonstrate that indeed it is possible for adversaries to exploit computational learning mechanism such as reinforcement learning (RL) to maximize the influence of socialbots while avoiding being detected.
TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation
Recent progress in generative language models has enabled machines to generate astonishingly realistic texts.
MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics Education
Due to the nature of mathematical texts, which often use domain specific vocabulary along with equations and math symbols, we posit that the development of a new BERT model for mathematics would be useful for many mathematical downstream tasks.
Large-Scale Data-Driven Airline Market Influence Maximization
On top of the prediction models, we define a budget-constrained flight frequency optimization problem to maximize the market influence over 2, 262 routes.
Classifying Math KCs via Task-Adaptive Pre-Trained BERT
Educational content labeled with proper knowledge components (KCs) are particularly useful to teachers or content organizers.
MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories
Automated metaphor detection is a challenging task to identify metaphorical expressions of words in a sentence.
Detecting micro fractures with X-ray computed tomography
The segmentation results from all five methods are compared to each other in terms of segmentation quality and time efficiency.
A Sweet Rabbit Hole by DARCY: Using Honeypots to Detect Universal Trigger's Adversarial Attacks
The Universal Trigger (UniTrigger) is a recently-proposed powerful adversarial textual attack method.
SHIELD: Defending Textual Neural Networks against Multiple Black-Box Adversarial Attacks with Stochastic Multi-Expert Patcher
Even though several methods have proposed to defend textual neural network (NN) models against black-box adversarial attacks, they often defend against a specific text perturbation strategy and/or require re-training the models from scratch.
Achieving User-Side Fairness in Contextual Bandits
Personalized recommendation based on multi-arm bandit (MAB) algorithms has shown to lead to high utility and efficiency as it can dynamically adapt the recommendation strategy based on feedback.
MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models
In recent years, the proliferation of so-called "fake news" has caused much disruptions in society and weakened the news ecosystem.
CoAID: COVID-19 Healthcare Misinformation Dataset
As the COVID-19 virus quickly spreads around the world, unfortunately, misinformation related to COVID-19 also gets created and spreads like wild fire.
Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements
In recent years, disinformation including fake news, has became a global phenomenon due to its explosive growth, particularly on social media.
GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model's Prediction
Despite the recent development in the topic of explainable AI/ML for image and text data, the majority of current solutions are not suitable to explain the prediction of neural network models when the datasets are tabular and their features are in high-dimensional vectorized formats.
Deep Reinforcement Learning for Personalized Search Story Recommendation
In recent years, \emph{search story}, a combined display with other organic channels, has become a major source of user traffic on platforms such as e-commerce search platforms, news feed platforms and web and image search platforms.
FakeNewsNet: A Data Repository with News Content, Social Context and Dynamic Information for Studying Fake News on Social Media
However, fake news detection is a non-trivial task, which requires multi-source information such as news content, social context, and dynamic information.
Social and Information Networks
Regularizing Matrix Factorization with User and Item Embeddings for Recommendation
Following recent successes in exploiting both latent factor and word embedding models in recommendation, we propose a novel Regularized Multi-Embedding (RME) based recommendation model that simultaneously encapsulates the following ideas via decomposition: (1) which items a user likes, (2) which two users co-like the same items, (3) which two items users often co-liked, and (4) which two items users often co-disliked.
