4 citations · 5 across the 5 of their papers we have counts for
5 papers
GPT-HateCheck: Can LLMs Write Better Functional Tests for Hate Speech Detection?
Yiping Jin, Leo Wanner, Alexander Shvets
Online hate detection suffers from biases incurred in data sampling, annotation, and model pre-training. Therefore, measuring the averaged performance over all examples in held-out…
Towards Weakly-Supervised Hate Speech Classification Across Datasets
Yiping Jin, Leo Wanner, Vishakha Laxman Kadam +1
As pointed out by several scholars, current research on hate speech (HS) recognition is characterized by unsystematic data creation strategies and diverging annotation schemata. Su…
Verifying the Robustness of Automatic Credibility Assessment
Piotr Przybyła, Alexander Shvets, Horacio Saggion
Text classification methods have been widely investigated as a way to detect content of low credibility: fake news, social media bots, propaganda, etc. Quite accurate models (likel…
Multilingual Extraction and Categorization of Lexical Collocations with Graph-aware Transformers
Luis Espinosa-Anke, Alexander Shvets, Alireza Mohammadshahi +2
Recognizing and categorizing lexical collocations in context is useful for language learning, dictionary compilation and downstream NLP. However, it is a challenging task due to th…
Concept Extraction Using Pointer-Generator Networks
Alexander Shvets, Leo Wanner
Concept extraction is crucial for a number of downstream applications. However, surprisingly enough, straightforward single token/nominal chunk-concept alignment or dictionary look…