Representation learning is an essential problem in a wide range of
applications and it is important for performing downstream tasks successfully.
In this paper, we propose a new model that learns coupled representations of
domains, intents, and slots by taking advantage of their hierarchical
dependency in a Spoken Language Understanding system. Our proposed model learns
the vector representation of intents based on the slots tied to these intents
by aggregating the representations of the slots. Similarly, the vector
representation of a domain is learned by aggregating the representations of the
intents tied to a specific domain. To the best of our knowledge, it is the
first approach to jointly learning the representations of domains, intents, and
slots using their hierarchical relationships. The experimental results
demonstrate the effectiveness of the representations learned by our model, as
evidenced by improved performance on the contextual cross-domain reranking
task.

本文提出了一种新模型，通过利用 Spoken Language Understanding 系统中域、意图和插槽之间的层次关系，学习耦合的表示。实验结果表明，该模型学习到的表示方法在交叉域重新排序任务上表现出更好的性能。