DSC IIT-ISM at SemEval-2020 Task 6: Boosting BERT with Dependencies for Definition Extraction
This work addresses definition extraction for NLP applications, but it is incremental as it builds on existing BERT methods with minor enhancements.
The paper tackled definition extraction by proposing a joint model combining BERT and a Text Level Graph Convolutional Network to incorporate dependencies, achieving better results than BERT alone and comparable to fine-tuned BERT in the DeftEval shared task.
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).