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.

翻译：近年来，基于多源画像信息的口语理解（Profile-based SLU）受到广泛关注，其目标是通过融合知识图谱、用户画像、上下文感知等各类辅助画像信息，消除用户表述中普遍存在的语义歧义。然而现有方法仅能独立建模不同类型的画像信息，既未考虑彼此间的关联性，也无法剔除其中的无关或冲突信息。针对上述问题，本文提出一种名为Pro-HAN的异构图注意力网络，实现对多种画像信息的联合推理。具体而言，我们设计了三类边结构（画像内连边、画像间连边、画像-表述连边）来捕捉多画像信息间的语义关联。在ProSLU数据集上，本方法在所有三项指标上均取得约8%的性能提升，刷新了当前最优结果。进一步的分析实验也验证了本方法在多源画像信息建模中的有效性。