  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.
