The research study of detecting multiple intents and filling slots is becoming more popular because of its relevance to complicated real-world situations. Recent advanced approaches, which are joint models based on graphs, might still face two potential issues: (i) the uncertainty introduced by constructing graphs based on preliminary intents and slots, which may transfer intent-slot correlation information to incorrect label node destinations, and (ii) direct incorporation of multiple intent labels for each token w.r.t. token-level intent voting might potentially lead to incorrect slot predictions, thereby hurting the overall performance. To address these two issues, we propose a joint model named MISCA. Our MISCA introduces an intent-slot co-attention mechanism and an underlying layer of label attention mechanism. These mechanisms enable MISCA to effectively capture correlations between intents and slot labels, eliminating the need for graph construction. They also facilitate the transfer of correlation information in both directions: from intents to slots and from slots to intents, through multiple levels of label-specific representations, without relying on token-level intent information. Experimental results show that MISCA outperforms previous models, achieving new state-of-the-art overall accuracy performances on two benchmark datasets MixATIS and MixSNIPS. This highlights the effectiveness of our attention mechanisms.

翻译：多意图检测与槽位填充的研究因其与复杂现实场景的相关性而日益受到关注。现有基于图的先进联合模型仍可能面临两个潜在问题：（i）基于初步意图和槽位构建图结构所引入的不确定性，可能导致意图-槽位关联信息被传递到错误的标签节点；（ii）针对每个词元直接采用多意图标签的词元级意图投票可能引发错误的槽位预测，从而损害整体性能。为解决这两个问题，我们提出名为MISCA的联合模型。该模型引入意图-槽位协同注意力机制和底层标签注意力机制，能够有效捕获意图与槽位标签之间的关联，无需构建图结构。同时，通过多层标签特定表示，该机制可双向传递关联信息（从意图到槽位与从槽位到意图），且不依赖词元级意图信息。实验结果表明，MISCA在MixATIS和MixSNIPS两个基准数据集上均超越现有模型，取得了新的整体准确率最优性能，充分验证了我们注意力机制的有效性。