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.

我们提出了一种名为 MISCA 的联合模型，它引入了一个意图 - 槽相互注意机制和一个标签注意机制的底层层次，可以有效捕捉意图和槽标签之间的关联，消除了图构建的需要，并通过多个层次的标签特定表示从意图到槽和从槽到意图传递关联信息，从而在两个基准数据集 MixATIS 和 MixSNIPS 上实现了新的最新的整体准确性性能，突显了我们注意机制的有效性。