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

TL;DR

Pro-HAN introduces a heterogeneous graph attention network that effectively models interrelationships among multiple profile sources to improve spoken language understanding accuracy.

Contribution

It proposes a novel graph-based approach to integrate diverse profile information, capturing their interrelations for enhanced SLU performance.

Findings

Achieved approximately 8% improvement on ProSLU dataset

Established new state-of-the-art results

Validated effectiveness in modeling multi-source profile data

Abstract

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.