인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
The purpose of this study is to assess the performance of semantic role labeling (SRL) predicted by the neural language models (NLMs, or Transformer-based pre-trained models) of Korean. First, the study built two models: the KR-BERT-BiLSTM-CRF model and the KR-BERT-Verb Position Feature (VPF)-BiLSTM-CRF model. The results from testing these two models show that the KR-BERT-VPF-BiLSTM-CRF model (67.3%) outperformed the KR-BERT-BiLSTM-CRF model (66.4%). In addition, this study examined which hidden layer improved the performance of NLMs during training. As expected, the NLM that was trained on the last hidden layer performed better than other alternative options such as the second-to-last-hidden layer and the concatenated last four layers. Thus, this study renders support to the general observation that an NLM should be trained on the last hidden layer to reach the highest performance. This study is meaningful since it is the first attempt to investigate which hidden layer is useful to train NLMs in SRL tasks of Korean.
#semantic role labeling
#Korean neural language model
#performance assessment
#layer-wise analysis
#heatmap analysis
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목차
- 1. Introduction
- 2. Previous studies
- 3. Layer-wise analysis
- 4. KR-BERT for Semantic Role Labeling
- 5. Discussion
- 6. Conclusion
- References
참고문헌
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