Abstract
We describe the system submitted to SemEval-2020 Task 6, Subtask 1. The aim of this subtask is to predict whether a given sentence contains a definition or not. Unsurprisingly, we found that strong results can be achieved by fine-tuning a pre-trained BERT language model. In this paper, we analyze the performance of this strategy. Among others, we show that results can be improved by using a two-step fine-tuning process, in which the BERT model is first fine-tuned on the full training set, and then further specialized towards a target domain.
Cite
CITATION STYLE
Jeawak, S. S., Espinosa-Anke, L., & Schockaert, S. (2020). Cardiff University at SemEval-2020 Task 6: Fine-tuning BERT for Domain-Specific Definition Classification. In 14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings (pp. 361–366). International Committee for Computational Linguistics. https://doi.org/10.18653/v1/2020.semeval-1.44
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