{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-learning-models-for-multilingual-hate","title":"Deep Learning Models for Multilingual Hate Speech Detection","arxiv_id":"2004.06465","date":"2020-04-14","proceeding":null,"authors":["Sai Saketh Aluru","Binny Mathew","Punyajoy Saha","Animesh Mukherjee"],"abstract":"Hate speech detection is a challenging problem with most of the datasets available in only one language: English. In this paper, we conduct a large scale analysis of multilingual hate speech in 9 languages from 16 different sources. We observe that in low resource setting, simple models such as LASER embedding with logistic regression performs the best, while in high resource setting BERT based models perform better. In case of zero-shot classification, languages such as Italian and Portuguese achieve good results. Our proposed framework could be used as an efficient solution for low-resource languages. These models could also act as good baselines for future multilingual hate speech detection tasks. We have made our code and experimental settings public for other researchers at https://github.com/punyajoy/DE-LIMIT.","url_abs":"https://arxiv.org/abs/2004.06465v3","url_pdf":"https://arxiv.org/pdf/2004.06465v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-learning-models-for-multilingual-hate","repo_url":"https://github.com/punyajoy/DE-LIMIT","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"deep-learning-models-for-multilingual-hate","repo_url":"https://github.com/DataforGoodIsrael/DetectHateSpeech","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"deep-learning-models-for-multilingual-hate","repo_url":"https://github.com/hate-alert/DE-LIMIT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"hate-speech-detection","task_name":"Hate Speech Detection"},{"task_slug":"question-similarity","task_name":"Question Similarity"},{"task_slug":"zero-shot-learning","task_name":"Zero-Shot Learning"},{"task_slug":null,"task_name":"zero-shot-classification"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bert","method_name":"BERT"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"logistic-regression","method_name":"Logistic Regression"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":null,"method_name":"Travel"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/hate-speech-detection-on-automatic","task":"Hate Speech Detection","dataset":"Automatic Misogynistic Identification","model":"mBert","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy":"0.832"},"uses_additional_data":false},{"leaderboard":"/sota/question-similarity-on-q2q-arabic-benchmark","task":"Question Similarity","dataset":"Q2Q Arabic Benchmark","model":"mBert","rank_in_archive_order":3,"of":3,"metrics":{"F1 score":"0.8365"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2004.06465","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}