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Found 14 papers, 1 papers with code
NaijaHate: Evaluating Hate Speech Detection on Nigerian Twitter Using Representative Data
To address the global issue of hateful content proliferating in online platforms, hate speech detection (HSD) models are typically developed on datasets collected in the United States, thereby failing to generalize to English dialects from the Majority World.
Designing Informative Metrics for Few-Shot Example Selection
This approach avoids the training of a dedicated model for selection of examples, and instead uses certain metrics to align the syntactico-semantic complexity of test sentences and examples.
Generation of a Compendium of Transcription Factor Cascades and Identification of Potential Therapeutic Targets using Graph Machine Learning
We constructed a knowledge graph of 81, 488 unique TF cascades, with the longest cascade consisting of 62 TFs.
Fine-grained prediction of food insecurity using news streams
Anticipating the outbreak of a food crisis is crucial to efficiently allocate emergency relief and reduce human suffering.
Learning Faithful Representations of Causal Graphs
By incorporating these faithfulness properties, we learn text embeddings that are 31. 3{\%} more faithful to human validated causal graphs with about 800K and 200K causal links and achieve 21. 1{\%} better Precision-Recall AUC in a link prediction fine-tuning task.
DICE: Deep Significance Clustering for Outcome-Aware Stratification
Following the representation learning and clustering steps, we embed the objective function in DICE with a constraint which requires a statistically significant association between the outcome and cluster membership of learned representations.
Beyond The Text: Analysis of Privacy Statements through Syntactic and Semantic Role Labeling
This paper formulates a new task of extracting privacy parameters from a privacy policy, through the lens of Contextual Integrity, an established social theory framework for reasoning about privacy norms.
Identifying Predictive Causal Factors from News Streams
We propose a new framework to uncover the relationship between news events and real world phenomena.
What is Fair? Exploring Pareto-Efficiency for Fairness Constrained Classifiers
The potential for learned models to amplify existing societal biases has been broadly recognized.
RECIPE: Applying Open Domain Question Answering to Privacy Policies
We describe our experiences in using an open domain question answering model (Chen et al., 2017) to evaluate an out-of-domain QA task of assisting in analyzing privacy policies of companies.
Unsupervised Word Influencer Networks from News Streams
We present Word Influencer Networks (WIN), a graph framework to extract longitudinal temporal relationships between any pair of informative words from news streams.
Relationship Extraction (Distant Supervised)
Stock Price Prediction
A Model-based Projection Technique for Segmenting Customers
We consider the problem of segmenting a large population of customers into non-overlapping groups with similar preferences, using diverse preference observations such as purchases, ratings, clicks, etc.
Reputation-based Worker Filtering in Crowdsourcing
In this paper, we study the problem of aggregating noisy labels from crowd workers to infer the underlying true labels of binary tasks.
