Papers by Chul Sung
Pre-Training BERT on Domain Resources for Short Answer Grading (D19-1)
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| Challenge: | Pre-trained contextualized representations have achieved state-of-the-art results on multiple downstream NLP tasks by fine-tuning with task-specific data. |
| Approach: | They propose to augment domain-specific data by using labeled short answering grading data for further enhancement of the pre-trained language model. |
| Outcome: | The proposed model can be enhanced by augmenting data from domain-specific resources like textbooks and labeled short answering grading data. |
CNNBiF: CNN-based Bigram Features for Named Entity Recognition (2021.findings-emnlp)
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| Challenge: | Named entity recognition tasks require a self-attention mechanism with unconstrained length that fails to capture local dependencies. |
| Approach: | They propose a joint training objective which better captures the semantics of words corresponding to the same entity by augmenting the objective with a group-consistency loss component. |
| Outcome: | The proposed model achieves a test F1 of 93.98 with a single transformer model. |