# Pro-HAN: A Heterogeneous Graph Attention Network for Profile-Based Spoken Language Understanding

arXiv: https://arxiv.org/abs/2402.03900 · HTML: https://mlsanity.com/paper/2402.03900/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [How Attentive are Graph Attention Networks?](https://mlsanity.com/paper/2105.14491/) (2021)
- [Key-Value Retrieval Networks for Task-Oriented Dialogue](https://mlsanity.com/paper/1705.05414/) (2017)
- [Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/1609.01454/) (2016)
- [A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding](https://mlsanity.com/paper/1909.02188/) (2019)
- [A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/1907.00390/) (2019)
- [Joint Slot Filling and Intent Detection via Capsule Neural Networks](https://mlsanity.com/paper/1812.09471/) (2018)
- [A Co-Interactive Transformer for Joint Slot Filling and Intent Detection](https://mlsanity.com/paper/2010.03880/) (2020)
- [A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling](https://mlsanity.com/paper/1812.10235/) (2018)
- [A Survey on Spoken Language Understanding: Recent Advances and New Frontiers](https://mlsanity.com/paper/2103.03095/) (2021)
- [Global-Locally Self-Attentive Dialogue State Tracker](https://mlsanity.com/paper/1805.09655/) (2018)
- [CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding](https://mlsanity.com/paper/1909.06937/) (2019)
- [Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering](https://mlsanity.com/paper/1805.04893/) (2018)
- [Injecting Word Information with Multi-Level Word Adapter for Chinese Spoken Language Understanding](https://mlsanity.com/paper/2010.03903/) (2020)
- [Text is no more Enough! A Benchmark for Profile-based Spoken Language Understanding](https://mlsanity.com/paper/2112.11953/) (2021)

## Built on by (most cited first, 2 of 2)

- [CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding](https://mlsanity.com/paper/2406.10505/) (2024)
- [AFD-SLU: Adaptive Feature Distillation for Spoken Language Understanding](https://mlsanity.com/paper/2509.04821/) (2025)

## Abstract (arXiv)

Recently, Profile-based Spoken Language Understanding (SLU) has gained increasing attention, which aims to incorporate various types of supplementary profile information (i.e., Knowledge Graph, User Profile, Context Awareness) to eliminate the prevalent ambiguities in user utterances. However, existing approaches can only separately model different profile information, without considering their interrelationships or excluding irrelevant and conflicting information within them. To address the above issues, we introduce a Heterogeneous Graph Attention Network to perform reasoning across multiple Profile information, called Pro-HAN. Specifically, we design three types of edges, denoted as intra-Pro, inter-Pro, and utterance-Pro, to capture interrelationships among multiple Pros. We establish a new state-of-the-art on the ProSLU dataset, with an improvement of approximately 8% across all three metrics. Further analysis experiments also confirm the effectiveness of our method in modeling multi-source profile information.