Abstract
Textual entailment, also known as natural language inference, is a branch of natural language processing (NLP) that examines the semantics and meaning of phrases and text excerpts in order to ascertain whether a hypothesis may be drawn from a premises. This study examines how well five distinct deep learning techniques work (BiLSTM, RoBERTa, GRU, Bi-GRU, and MLP) using different word embeddings (GloVe and BERT) in identifying textual entailment in SRS documents. The dataset used in this research was obtained from the use case diagrams (UCD) and the use case specifications (UCS) written in English and the SNLI corpus. Overall, the BiLSTM-GloVe model achieved the best results compared to other models on the proposed dataset with an accuracy of 49%, an F1-score of 46%, a precision of 46%, and a recall of 59%.
Original language | English |
---|---|
Title of host publication | 2024 International Seminar on Intelligent Technology and Its Applications |
Subtitle of host publication | Collaborative Innovation: A Bridging from Academia to Industry towards Sustainable Strategic Partnership, ISITIA 2024 - Proceeding |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 669-674 |
Number of pages | 6 |
Edition | 2024 |
ISBN (Electronic) | 9798350378573 |
DOIs | |
Publication status | Published - 2024 |
Event | 25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024 - Hybrid, Mataram, Indonesia Duration: 10 Jul 2024 → 12 Jul 2024 |
Conference
Conference | 25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024 |
---|---|
Country/Territory | Indonesia |
City | Hybrid, Mataram |
Period | 10/07/24 → 12/07/24 |
Keywords
- Comparative Study
- Deep Learning
- SRS Documents
- Text Entailment
- Use Case Diagram