DSC IIT-ISM at SemEval-2020 Task 6: Boosting BERT with Dependencies for Definition Extraction

09/17/2020 ∙ by Aadarsh Singh, et al. ∙ 0 ∙ share

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).

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DSC IIT-ISM at SemEval-2020 Task 6: Boosting BERT with Dependencies for Definition Extraction

Authors

Investigating the Successes and Failures of BERT for Passage Re-Ranking

LT@Helsinki at SemEval-2020 Task 12: Multilingual or language-specific BERT?

Understanding BERT performance in propaganda analysis

Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution

Evaluating robustness of language models for chief complaint extraction from patient-generated text

Fast and Accurate Deep Bidirectional Language Representations for Unsupervised Learning

ColBERT: Using BERT Sentence Embedding for Humor Detection

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