Pro-HAN: A Heterogeneous Graph Attention Network for Profile-Based
  Spoken Language Understanding
Dechuan Teng Chunlin Lu Xiao Xu Wanxiang Che Libo Qin
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.