arXiv:1812.06700
공식 코드 · Papers with Code
Hateminers : Detecting Hate speech against Women
원문 초록
With the online proliferation of hate speech, there is an urgent need for systems that can detect such harmful content. In this paper, We present the machine learning models developed for the Automatic Misogyny Identification (AMI) shared task at EVALITA 2018. We generate three types of features: Sentence Embeddings, TF-IDF Vectors, and BOW Vectors to represent each tweet. These features are then concatenated and fed into the machine learning models. Our model came First for the English Subtask A and Fifth for the English Subtask B. We release our winning model for public use and it's available at https://github.com/punyajoy/Hateminers-EVALITA.
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AmpleHate: Amplifying the Attention for Versatile Implicit Hate Detection
LLMs Are Not Scorers: Rethinking MT Evaluation with Generation-Based Methods
Contrastive Prompting Enhances Sentence Embeddings in LLMs through Inference-Time Steering
Sentence Embeddings as an intermediate target in end-to-end summarisation
JTCSE: Joint Tensor-Modulus Constraints and Cross-Attention for Unsupervised Contrastive Learning of Sentence Embeddings
Information Leakage of Sentence Embeddings via Generative Embedding Inversion Attacks
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