Siamese-based metric joint learning for intent detection and slot filling using triplet loss optimization.

ABSTRACT: Spoken language understanding (SLU) relies on intent detection and slot filling to interpret user utterances accurately. However, existing joint learning frameworks struggle to generalize across minority intent classes and paraphrase queries. They depend heavily on token-level embeddings and classification losses such as cross-entropy, which do not explicitly model semantic similarity. To address this limitation, this study proposes a Siamese-Based Metric Joint Learning model for Intent Detection and Slot Filling (SBJLIS). The model uses triplet loss optimization to enhance semantic distance learning between utterances. Unlike standard cross-entropy training, triplet loss enforces separation between dissimilar classes and brings semantically related sentences closer in the embedding space.

SUBMITTER: Muhammad YI 

PROVIDER: S-EPMC12816612 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature