ANDES at SemEval-2020 Task 12: A jointly-trained BERT multilingual model for offensive language detection
| Autor: | Juan Manuel Pérez, Aymé Arango, Franco M. Luque |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language Language identification Computer science business.industry Arabic Turkish Offensive computer.software_genre language.human_language Code (semiotics) SemEval Task (project management) Danish language Artificial intelligence business computer Computation and Language (cs.CL) Natural language processing |
| Zdroj: | SemEval@COLING |
| DOI: | 10.48550/arxiv.2008.06408 |
| Popis: | This paper describes our participation in SemEval-2020 Task 12: Multilingual Offensive Language Detection. We jointly-trained a single model by fine-tuning Multilingual BERT to tackle the task across all the proposed languages: English, Danish, Turkish, Greek and Arabic. Our single model had competitive results, with a performance close to top-performing systems in spite of sharing the same parameters across all languages. Zero-shot and few-shot experiments were also conducted to analyze the transference performance among these languages. We make our code public for further research Comment: Github repo: https://github.com/finiteautomata/offenseval2020 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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