activity
20202026
most citedCharacter-level HyperNetworks for Hate Speech Detection

16 citations · 17 across the 7 of their papers we have counts for

collaborators

8 papers

cs.IR2026

The Overlooked Role of Graded Relevance Thresholds in Multilingual Dense Retrieval

Tomer Wullach, Ori Shapira, Amir DN Cohen

Dense retrieval models are typically fine-tuned with contrastive learning objectives that require binary relevance judgments, even though relevance is inherently graded. We analyze…

cs.CL2023★ 1 cited

Generative AI for Hate Speech Detection: Evaluation and Findings

Sagi Pendzel, Tomer Wullach, Amir Adler +1

Automatic hate speech detection using deep neural models is hampered by the scarcity of labeled datasets, leading to poor generalization. To mitigate this problem, generative AI ha…

cs.CL2023

Optimized Tokenization for Transcribed Error Correction

Tomer Wullach, Shlomo E. Chazan

The challenges facing speech recognition systems, such as variations in pronunciations, adverse audio conditions, and the scarcity of labeled data, emphasize the necessity for a po…

cs.CL2022

Don't Be So Sure! Boosting ASR Decoding via Confidence Relaxation

Tomer Wullach, Shlomo E. Chazan

Automatic Speech Recognition (ASR) systems frequently use a search-based decoding strategy aiming to find the best attainable transcript by considering multiple candidates. One pro…

cs.CL2022

Enhancing Speech Recognition Decoding via Layer Aggregation

Tomer Wullach, Shlomo E. Chazan

Recently proposed speech recognition systems are designed to predict using representations generated by their top layers, employing greedy decoding which isolates each timestep fro…

cs.CL2021★ 16 cited

Character-level HyperNetworks for Hate Speech Detection

Tomer Wullach, Amir Adler, Einat Minkov

The massive spread of hate speech, hateful content targeted at specific subpopulations, is a problem of critical social importance. Automated methods of hate speech detection typic…

cs.CL2021

Fight Fire with Fire: Fine-tuning Hate Detectors using Large Samples of Generated Hate Speech

Tomer Wullach, Amir Adler, Einat Minkov

Automatic hate speech detection is hampered by the scarcity of labeled datasetd, leading to poor generalization. We employ pretrained language models (LMs) to alleviate this data b…

cs.CL2020

Towards Hate Speech Detection at Large via Deep Generative Modeling

Tomer Wullach, Amir Adler, Einat Minkov

Hate speech detection is a critical problem in social media platforms, being often accused for enabling the spread of hatred and igniting physical violence. Hate speech detection r…