# A Self-Attention Joint Model for Spoken Language Understanding in Situational Dialog Applications

arXiv: https://arxiv.org/abs/1905.11393 · HTML: https://mlsanity.com/paper/1905.11393/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2019.

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

- [Joint Intent Detection and Slot Filling with Wheel-Graph Attention Networks](https://mlsanity.com/paper/2102.04610/) (2021)
- [Calibrate and Refine! A Novel and Agile Framework for ASR-error Robust Intent Detection](https://mlsanity.com/paper/2205.11008/) (2022)
- [A Unified Framework for Slot based Response Generation in a Multimodal Dialogue System](https://mlsanity.com/paper/2305.17433/) (2023)
- [A survey of joint intent detection and slot-filling models in natural language understanding](https://mlsanity.com/paper/2101.08091/) (2021)

## Abstract (arXiv)

Spoken language understanding (SLU) acts as a critical component in goal-oriented dialog systems. It typically involves identifying the speakers intent and extracting semantic slots from user utterances, which are known as intent detection (ID) and slot filling (SF). SLU problem has been intensively investigated in recent years. However, these methods just constrain SF results grammatically, solve ID and SF independently, or do not fully utilize the mutual impact of the two tasks. This paper proposes a multi-head self-attention joint model with a conditional random field (CRF) layer and a prior mask. The experiments show the effectiveness of our model, as compared with state-of-the-art models. Meanwhile, online education in China has made great progress in the last few years. But there are few intelligent educational dialog applications for students to learn foreign languages. Hence, we design an intelligent dialog robot equipped with different scenario settings to help students learn communication skills.