https://scholars.lib.ntu.edu.tw/handle/123456789/632519
標題: | NTU_NLP at SemEval-2020 Task 12: Identifying Offensive Tweets Using Hierarchical Multi-Task Learning Approach | 作者: | Chen P.-C Huang H.-H HSIN-HSI CHEN |
公開日期: | 2020 | 起(迄)頁: | 2105-2110 | 來源出版物: | 14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings | 摘要: | This paper presents our hierarchical multi-task learning (HMTL) and multi-task learning (MTL) approaches for improving the text encoder in Sub-tasks A, B, and C of Multilingual Offensive Language Identification in Social Media (SemEval-2020 Task 12). We show that using the MTL approach can greatly improve the performance of complex problems, i.e. Sub-tasks B and C. Coupled with a hierarchical approach, the performances are further improved. Overall, our best model, HMTL outperforms the baseline model by 3% and 2% of Macro F-score in Sub-tasks B and C of OffensEval 2020, respectively. © 2020 14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings. All rights reserved. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094796692&partnerID=40&md5=df617b03ce4d569a189e569f81ce2c5f https://scholars.lib.ntu.edu.tw/handle/123456789/632519 |
SDG/關鍵字: | C (programming language); Computational linguistics; Linearization; Natural language processing systems; Semantics; Best model; Complex problems; Hierarchical approach; Language identification; Learning approach; Multitask learning; Offensive languages; Performance; Social media; Subtask; Learning systems |
顯示於: | 資訊工程學系 |
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