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Found 9 papers, 2 papers with code
TNT: Text Normalization based Pre-training of Transformers for Content Moderation
In this work, we present a new language pre-training model TNT (Text Normalization based pre-training of Transformers) for content moderation.
Automatic Labeling of Parkinson’s Disease Gait Videos with Weak Supervision
The method obtained state-of-the-art results on the Human3. 6M dataset.
SmartGD: A GAN-Based Graph Drawing Framework for Diverse Aesthetic Goals
There are a few existing methods that have attempted to develop a flexible solution for optimizing different aesthetic aspects measured by different aesthetic criteria.
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.
BERT-Beta: A Proactive Probabilistic Approach to Text Moderation
Text moderation for user generated content, which helps to promote healthy interaction among users, has been widely studied and many machine learning models have been proposed.
TSI: an Ad Text Strength Indicator using Text-to-CTR and Semantic-Ad-Similarity
To realize this opportunity, we propose an ad text strength indicator (TSI) which: (i) predicts the click-through-rate (CTR) for an input ad text, (ii) fetches similar existing ads to create a neighborhood around the input ad, (iii) and compares the predicted CTRs in the neighborhood to declare whether the input ad is strong or weak.
DeepGD: A Deep Learning Framework for Graph Drawing Using GNN
In this paper, we propose a Convolutional Graph Neural Network based deep learning framework, DeepGD, which can draw arbitrary graphs once trained.
Political Posters Identification with Appearance-Text Fusion
In this paper, we propose a method that efficiently utilizes appearance features and text vectors to accurately classify political posters from other similar political images.
HABERTOR: An Efficient and Effective Deep Hatespeech Detector
HABERTOR inherits BERT's architecture, but is different in four aspects: (i) it generates its own vocabularies and is pre-trained from the scratch using the largest scale hatespeech dataset; (ii) it consists of Quaternion-based factorized components, resulting in a much smaller number of parameters, faster training and inferencing, as well as less memory usage; (iii) it uses our proposed multi-source ensemble heads with a pooling layer for separate input sources, to further enhance its effectiveness; and (iv) it uses a regularized adversarial training with our proposed fine-grained and adaptive noise magnitude to enhance its robustness.
