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LTL-UDE at SemEval-2019 Task 6: BERT and Two-Vote Classification for Categorizing Offensiveness

This paper describes LTL-UDE’s systems for the SemEval 2019 Shared Task 6. We present results for Subtask A and C. In Subtask A, we experiment with an embedding representation of postings and use a Multi-Layer Perceptron and BERT to categorize postings. Our best result reaches the 10th place (out of 103) using BERT. In Subtask C, we applied a two-vote classification approach with minority fallback, which is placed on the 19th rank (out of 65).

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Conference:
NAACL HLT 2019, 13th International Workshop on Semantic Evaluation (SemEval 2019), June 6–June 7, 2019 Minneapolis, Minnesota, USA
Date Issued:
2019
URN:
urn:nbn:de:hbz:464-20211019-094140-7
DOI:
10.18653/v1/S19-2121
Language:
English
Type of Resource:
Text
Collection:
E-Publications
Subject categories of the Deutsche Nationalbibliografie:
004 Data processing Computer sciences
Hosting institution:
Faculty of Engineering, Computer Science and Applied Cognitive Science, Computer Science, Language Technology Lab
accrual policy:
Aggarwal, P., Horsmann, T., Wojatzki, M., Zesch, T. (2019): LTL-UDE at SemEval-2019 Task 6: BERT and Two-Vote Classification for Categorizing Offensiveness. In: Proceedings of the 13th International Workshop on Semantic Evaluation (SemEval 2019), pp. 678-682. Association for Computational Linguistics. DOI: https://doi.org/10.18653/v1/S19-2121

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