SBERT-PRO: Predicate Oriented Sentence Embedding Model for Intent and Event Detection

Dongryul Ko, Jeayun Lee, Dahee Lee, Yuri Son, Sangmin Kim, Jaeeun Jang, Munhyeong Kim, Sanghyun Park, Jaieun Kim

http://doi.org/10.5626/JOK.2024.51.2.165

Intent detection is a crucial task in conversational systems for understanding user intentions. Additionally, event detection is vital for identifying important events within various texts, including news articles, social media posts, and reports. Among diverse approaches, the sentence embedding similarity-based method has been widely adopted to solve open-domain classification tasks. However, conventional embedding models tend to focus on specific keywords within a sentence and are not suitable for tasks that require a high-level semantic understanding of a sentence as opposed to a narrow focus on specific details within a sentence. This limitation becomes particularly evident in tasks such as intent detection, which requires a broader understanding of the intention of a sentence, and event detection, which requires an emphasis on actual events within a sentence. In this paper, we construct a training dataset suitable for intent and event detection using entity attribute information and entity relation information. Our approach is inspired by the significance of emphasizing the embedding of predicates, which unfold the content of a sentence, as opposed to focusing on entity attributes within a sentence. Furthermore, we suggest an adaptive learning strategy for the existing sentence embedding model and demonstrate that our proposed model, SBERT-PRO (PRedicate Oriented), outperforms conventional models