Search Results for author:
Found 8 papers, 3 papers with code
ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detection
Hate speech detection is complex; it relies on commonsense reasoning, knowledge of stereotypes, and an understanding of social nuance that differs from one culture to the next.
Meta AI at Arabic Hate Speech 2022: MultiTask Learning with Self-Correction for Hate Speech Classification
The tasks are to predict if a tweet contains (1) Offensive language; and whether it is considered (2) Hate Speech or not and if so, then predict the (3) Fine-Grained Hate Speech label from one of six categories.
A Review on Language Models as Knowledge Bases
Recently, there has been a surge of interest in the NLP community on the use of pretrained Language Models (LMs) as Knowledge Bases (KBs).
How to Learn and Represent Abstractions: An Investigation using Symbolic Alchemy
Alchemy is a new meta-learning environment rich enough to contain interesting abstractions, yet simple enough to make fine-grained analysis tractable.
Deep Spiking Neural Networks with Resonate-and-Fire Neurons
In this work, we explore a new Spiking Neural Network (SNN) formulation with Resonate-and-Fire (RAF) neurons (Izhikevich, 2001) trained with gradient descent via back-propagation.
The Emergence of Abstract and Episodic Neurons in Episodic Meta-RL
In this work, we analyze the reinstatement mechanism introduced by Ritter et al. (2018) to reveal two classes of neurons that emerge in the agent's working memory (an epLSTM cell) when trained using episodic meta-RL on an episodic variant of the Harlow visual fixation task.
Adapting MARBERT for Improved Arabic Dialect Identification: Submission to the NADI 2021 Shared Task
Tasks are to identify the geographic origin of short Dialectal (DA) and Modern Standard Arabic (MSA) utterances at the levels of both country and province.
Deep Diacritization: Efficient Hierarchical Recurrence for Improved Arabic Diacritization
We propose a novel architecture for labelling character sequences that achieves state-of-the-art results on the Tashkeela Arabic diacritization benchmark.
Ranked #3 on
Arabic Text Diacritization
on Tashkeela
