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

arXiv: https://arxiv.org/abs/1812.06083 · HTML: https://mlsanity.com/paper/1812.06083/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2018.

## Built on by (most cited first, 4 of 4)

- [Explainable Slot Type Attentions to Improve Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/2210.10227/) (2022)
- [A survey of joint intent detection and slot-filling models in natural language understanding](https://mlsanity.com/paper/2101.08091/) (2021)
- [A Robust Semantic Frame Parsing Pipeline on a New Complex Twitter Dataset](https://mlsanity.com/paper/2212.08987/) (2022)
- [Explainable and Accurate Natural Language Understanding for Voice Assistants and Beyond](https://mlsanity.com/paper/2309.14485/) (2023)

## Abstract (arXiv)

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.