Abstract
We explore the performance of Bidirectional Encoder Representations from Transformers (BERT) at definition extraction. We further propose a joint model of BERT and Text Level Graph Convolutional Network so as to incorporate dependencies into the model. Our proposed model produces better results than BERT and achieves comparable results to BERT with fine tuned language model in DeftEval (Task 6 of SemEval 2020), a shared task of classifying whether a sentence contains a definition or not (Subtask 1).- Anthology ID:
- 2020.semeval-1.93
- 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:
- 710–716
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.93
- DOI:
- 10.18653/v1/2020.semeval-1.93
- Cite (ACL):
- Aadarsh Singh, Priyanshu Kumar, and Aman Sinha. 2020. DSC IIT-ISM at SemEval-2020 Task 6: Boosting BERT with Dependencies for Definition Extraction. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 710–716, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- DSC IIT-ISM at SemEval-2020 Task 6: Boosting BERT with Dependencies for Definition Extraction (Singh et al., SemEval 2020)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2020.semeval-1.93.pdf
- Code
- dsciitism/SemEval-2020-Task-6
- Data
- DEFT Corpus