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Found 13 papers, 0 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.
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.
What's in a Name? -- Gender Classification of Names with Character Based Machine Learning Models
Gender information is no longer a mandatory input when registering for an account at many leading Internet companies.
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.
Repulsive Attention: Rethinking Multi-head Attention as Bayesian Inference
The neural attention mechanism plays an important role in many natural language processing applications.
Large-scale Gender/Age Prediction of Tumblr Users
In this paper, we propose graph based and deep learning models for age and gender predictions, which take into account user activities and content features.
A Deep Structural Model for Analyzing Correlated Multivariate Time Series
The seasonality component is approximated via a non-liner function of a set of Fourier terms, and the event components are learned by a simple linear function of regressor encoding the event dates.
Deep Generative Models for Relational Data with Side Information
Moreover, inference cost scales in the number of edges which is attractive for massive but sparse networks.
Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings
We present a scalable Bayesian multi-label learning model based on learning low-dimensional label embeddings.
Zero-Truncated Poisson Tensor Factorization for Massive Binary Tensors
We present a scalable Bayesian model for low-rank factorization of massive tensors with binary observations.
Scalable Bayesian Non-Negative Tensor Factorization for Massive Count Data
We present a Bayesian non-negative tensor factorization model for count-valued tensor data, and develop scalable inference algorithms (both batch and online) for dealing with massive tensors.
