DeepAI AI Chat
Log In Sign Up

Coupled Representation Learning for Domains, Intents and Slots in Spoken Language Understanding

by   JIhwan Lee, et al.

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.


page 1

page 2

page 3

page 4


Locale-agnostic Universal Domain Classification Model in Spoken Language Understanding

In this paper, we introduce an approach for leveraging available data ac...

The importance of fillers for text representations of speech transcripts

While being an essential component of spoken language, fillers (e.g."um"...

Multi-Domain Spoken Language Understanding Using Domain- and Task-Aware Parameterization

Spoken language understanding has been addressed as a supervised learnin...

Multi-Domain Adversarial Learning for Slot Filling in Spoken Language Understanding

The goal of this paper is to learn cross-domain representations for slot...

Relation learning in a neurocomputational architecture supports cross-domain transfer

People readily generalise prior knowledge to novel situations and stimul...

Capsule Networks for Low Resource Spoken Language Understanding

Designing a spoken language understanding system for command-and-control...

Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning

We use Bayesian optimization to learn curricula for word representation ...