CEA-Net: a co-interactive external attention network for joint intent detection and slot filling.

Intent detection and slot filling are two crucial tasks for spoken language understanding, and they are closely related. The accuracy of spoken language understanding depends strongly on the effectiveness of the interaction between intent and slot representations. However, previous studies have primarily focused on exploring the interaction of intent and slot representations within individual utterances while neglecting the relevance of different utterances. The paper proposes the CEA-Net, which utilizes co-interactive external attention as its core mechanism to effectively capture information from multiple utterances and perform information interaction between the two tasks. Experimental results demonstrate that the CEA-Net achieves competitive results on the ATIS and SNIPS benchmarks while reducing the number of parameters by about 44% compared with the previous best open-source approach. Furthermore, since our framework models the correlation of multiple utterances, it shows promising effectiveness and robustness even with limited training resources or datasets. [ABSTRACT FROM AUTHOR]