LCAN : A Label-Aware Contrastive Attention Network for Multi-Intent Recognition and Slot Filling in Task-Oriented Dialogue Systems (2025)

Abstract: Multi-intent utterances processing remains a persistent challenge due to intricate intent-slot dependencies and semantic ambiguities. Traditional methods struggle to model these complex interactions, particularly when handling overlapping slot structures across multiple intents. This paper introduces a label-aware contrastive attention network (LCAN), a joint modeling approach for multi-intent recognition and slot filling in task-oriented dialogue systems. LCAN addresses this issue by integrating label-aware attention and contrastive learning strategies, improving semantic understanding and generalization in multi-intent scenarios. Extensive experiments on the MixATIS and MixSNIPS datasets demonstrate LCAN’s superiority over existing models, achieving improved intent recognition and slot filling performance, particularly in handling overlapping or complex semantic structures in multi-intent settings.

## introduction task-oriented dialogue (tod) systems are critical for applications like virtual assistants and customer service automation. a key challenge lies in multiintent scenarios where single utterances express multiple intents and slots, requiring precise identification for optimal user experience @xcite . traditional methods @xcite treated intent recognition and slot filling as separate tasks. recent studies reveal their intrinsic connections, driving adoption of joint modeling to enable information sharing, reduce error propagation, and improve efficiency. early joint models combined crfs with cnns @xcite or grus @xcite , later enhanced by attention mechanisms @xcite . pre-trained models revolutionized the field, with joint bert @xcite leveraging bert embeddings. subse-quent innovations include syntax-enhanced transformers @xcite and bilinear attention for parallel intent-slot extraction @xcite . despite advances, multi-intent handling remains challenging. as shown in figure 1 , utterances like "what does ewr stand for, what airlines fly from burbank to denver and airports in new york?" contain multiple intents with distinct slot types. current models often fail to accurately disentangle and align these overlapping semantic components. as dialogue systems increasingly handle multiintent scenarios in practical applications, researchers have focused on effective identification and alignment of multiple intents and slots in complex interactions. @xcite first explored multi-intent recognition in spoken language understanding and introduced joint multi-intent modeling. @xcite proposed a joint framework with slot-gating mechanisms, though semantic ambiguity persisted due to single-vector guidance for multiple slot fillings. to address this limitation, @xcite developed a graph attention network (gat)-based interaction model establishing fine-grained intentslot mappings. however, its autoregressive structure constrained bidirectional information capture. subsequently, @xcite introduced a global-local graph framework to model complex intent-slot and slot-slot relations, enhancing joint modeling performance. xing and tsang (2022) proposed a heterogeneous graph with mutual "slot-to-intent" and "intent-to-slot" guidance, coupled with label-aware decoding for enhanced interaction. @xcite al. (2023a) we propose lcan (label-aware contrastive attention network), a novel joint model integrating label-aware attention with contrastive learning to address these issues. lcan enhances semantic understanding through contrastive intent-slot alignment while employing label-aware attention to reduce input ambiguity. our framework enables effective information sharing between tasks while minimizing semantic interference. experiments on mixatis and mixsnips datasets demonstrate lcan's superiority over state-of-the-art baselines, particularly in complex multi-intent scenarios. ablation studies confirm the critical role of contrastive learning, with component-wise analysis revealing performance contributions. in this paper, we make the following contributions:(1)we propose lcan, a joint model that combines label-aware attention and contrastive learning to improve multi-intent recognition and slot filling in task-oriented dialogue systems.(2)we demonstrate the effectiveness of lcan through experiments on two standard benchmark datasets, mix-atis and mixsnips, and compare its performance with existing state-of-the-art models.(3)we conduct ablation studies to analyze the contribution of each component of lcan and show that the contrastive learning modules play a key role in enhancing the model's performance. ## model design the lcan model is designed around a label-aware attention mechanism, which enhances semantic discrimination through contrastive supervision. it further introduces a collaborative attention module to enable bidirectional interaction between intents and slots for efficient joint modeling. as illustrated in figure 2 , the overall architecture consists of four main components: a text encoder, a label-aware attention mechanism, a contrastive learning module, and intent-slot decoders. ## conclusion we propose lcan, a novel joint model addressing multi-intent recognition and slot filling challenges in task-oriented dialogue systems. lcan integrates label-aware attention for precise inputlabel alignment and contrastive learning to mitigate semantic ambiguity among overlapping labels. evaluations on mixatis and mixsnips datasets demonstrate lcan's superiority over state-of-theart models, particularly in complex multi-intent scenarios requiring nuanced intent-slot interactions. ablation studies confirm the critical roles of contrastive learning and label-aware attention. while lcan achieves robust performance, future work could explore hierarchical attention mechanisms and optimized contrastive strategies for improved generalization to unseen, highly diverse dialogues. the framework establishes a scalable foundation for advancing joint modeling in multi-intent dialogue systems.