Search Results for author:
Found 9 papers, 2 papers with code
Capturing Covertly Toxic Speech via Crowdsourcing
We study the task of labeling covert or veiled toxicity in online conversations.
SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification
The paper describes the SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification (MAMI), which explores the detection of misogynous memes on the web by taking advantage of available texts and images.
Lost in Distillation: A Case Study in Toxicity Modeling
In an era of increasingly large pre-trained language models, knowledge distillation is a powerful tool for transferring information from a large model to a smaller one.
A New Generation of Perspective API: Efficient Multilingual Character-level Transformers
As such, it is crucial to develop models that are effective across a diverse range of languages, usages, and styles.
ReasonBERT: Pre-trained to Reason with Distant Supervision
We present ReasonBert, a pre-training method that augments language models with the ability to reason over long-range relations and multiple, possibly hybrid contexts.
Ranked #1 on
Semantic Parsing
on GraphQuestions
Embedding Semantic Taxonomies
A common step in developing an understanding of a vertical domain, e. g. shopping, dining, movies, medicine, etc., is curating a taxonomy of categories specific to the domain.
TURL: Table Understanding through Representation Learning
In this paper, we present TURL, a novel framework that introduces the pre-training/fine-tuning paradigm to relational Web tables.
Ranked #1 on
Column Type Annotation
on WikipediaGS-CTA
What is Fair? Exploring Pareto-Efficiency for Fairness Constrained Classifiers
The potential for learned models to amplify existing societal biases has been broadly recognized.
Fairness Sample Complexity and the Case for Human Intervention
We demonstrate that for a classifier to approach a definition of fairness in terms of specific sensitive variables, adequate subgroup population samples need to exist and the model dimensionality has to be aligned with subgroup population distributions.
