  Spoken Language Understanding (SLU) mainly involves two tasks, intent
detection and slot filling, which are generally modeled jointly in existing
works. However, most existing models fail to fully utilize co-occurrence
relations between slots and intents, which restricts their potential
performance. To address this issue, in this paper we propose a novel
Collaborative Memory Network (CM-Net) based on the well-designed block, named
CM-block. The CM-block firstly captures slot-specific and intent-specific
features from memories in a collaborative manner, and then uses these enriched
features to enhance local context representations, based on which the
sequential information flow leads to more specific (slot and intent) global
utterance representations. Through stacking multiple CM-blocks, our CM-Net is
able to alternately perform information exchange among specific memories, local
contexts and the global utterance, and thus incrementally enriches each other.
We evaluate the CM-Net on two standard benchmarks (ATIS and SNIPS) and a
self-collected corpus (CAIS). Experimental results show that the CM-Net
achieves the state-of-the-art results on the ATIS and SNIPS in most of
criteria, and significantly outperforms the baseline models on the CAIS.
Additionally, we make the CAIS dataset publicly available for the research
community.
