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.- Anthology ID:
- 2020.semeval-1.44
- Volume:
- Proceedings of the Fourteenth Workshop on Semantic Evaluation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona (online)
- Editors:
- Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- International Committee for Computational Linguistics
- Note:
- Pages:
- 361–366
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.44
- DOI:
- 10.18653/v1/2020.semeval-1.44
- Cite (ACL):
- Shelan Jeawak, Luis Espinosa-Anke, and Steven Schockaert. 2020. Cardiff University at SemEval-2020 Task 6: Fine-tuning BERT for Domain-Specific Definition Classification. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 361–366, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- Cardiff University at SemEval-2020 Task 6: Fine-tuning BERT for Domain-Specific Definition Classification (Jeawak et al., SemEval 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.semeval-1.44.pdf