@inproceedings{jeawak-etal-2020-cardiff,
title = "{C}ardiff {U}niversity at {S}em{E}val-2020 Task 6: Fine-tuning {BERT} for Domain-Specific Definition Classification",
author = "Jeawak, Shelan and
Espinosa-Anke, Luis and
Schockaert, Steven",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.44",
doi = "10.18653/v1/2020.semeval-1.44",
pages = "361--366",
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.",
}
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%0 Conference Proceedings
%T Cardiff University at SemEval-2020 Task 6: Fine-tuning BERT for Domain-Specific Definition Classification
%A Jeawak, Shelan
%A Espinosa-Anke, Luis
%A Schockaert, Steven
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F jeawak-etal-2020-cardiff
%X 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.
%R 10.18653/v1/2020.semeval-1.44
%U https://aclanthology.org/2020.semeval-1.44
%U https://doi.org/10.18653/v1/2020.semeval-1.44
%P 361-366
Markdown (Informal)
[Cardiff University at SemEval-2020 Task 6: Fine-tuning BERT for Domain-Specific Definition Classification](https://aclanthology.org/2020.semeval-1.44) (Jeawak et al., SemEval 2020)
ACL