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Using Sentence Embeddings to identify Hate Speech against Immigrants and Women on TwitterAuthors: Vijaysaradhi Indurthi,Bakhtiyar Syed,Manish Shrivastava,Nikhil Chakravartula,Manish Gupta,Vasudeva Varma Conference: 13th International Workshop on Semantic Evaluation (SemEval-2019 2019) Location Minneapolis, USA Date: 2019-06-06 Report no: IIIT/TR/2019/97 AbstractThis paper describes our system (Fermi) for Task 5 of SemEval-2019: HatEval: Multilingual Detection of Hate Speech Against Immigrants and Women on Twitter. We participated in the subtask A for English and ranked first in the evaluation on the test set. We evaluate the quality of multiple sentence embeddings and explore multiple training models to evaluate the performance of simple yet effective embedding-ML combination algorithms. Our team - Fermi’s model achieved an accuracy of 65.00% for English language in task A. Our models, which use pretrained Universal Encoder sentence embeddings for transforming the input and SVM (with RBF kernel) for classification, scored first position (among 68) in the leaderboard on the test set for Subtask A in English language. In this paper we provide a detailed description of the approach, as well as the results obtained in the task. Full paper: pdf Centre for Search and Information Extraction Lab |
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