MISCA：一种采用意图-槽位协同注意力的多意图检测与槽位填充联合模型
MISCA: A Joint Model for Multiple Intent Detection and Slot Filling with Intent-Slot Co-Attention
AI总结：
本文提出 MISCA 联合模型，通过意图-槽位协同注意力与标签注意力避免图构建和 token 级意图投票，并在 MixATIS、MixSNIPS 上取得最先进性能。
摘要：
由于与复杂现实场景密切相关，检测多个意图并填充槽位的研究正变得越来越受欢迎。近期先进的方法是基于图的联合模型，但仍可能面临两个潜在问题：（i）基于初步意图和槽位构建图所引入的不确定性，可能将意图-槽位关联信息传递到错误的标签节点目的地；（ii）就 token 级意图投票而言，将多个意图标签直接并入每个 token，可能导致错误的槽位预测，从而损害整体性能。为解决这两个问题，我们提出了一个名为 MISCA 的联合模型。我们的 MISCA 引入了意图-槽位协同注意力机制以及底层的标签注意力机制。这些机制使 MISCA 能够有效捕捉意图与槽位标签之间的关联，无需构建图。它们还通过多层标签特定表示，促进关联信息在意图到槽位以及槽位到意图两个方向上的传递，而不依赖 token 级意图信息。实验结果表明，MISCA 优于以往模型，在两个基准数据集 MixATIS 和 MixSNIPS 上取得了新的最先进总体准确率性能。这凸显了我们所提注意力机制的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
