Profile-based intent detection and slot filling are important tasks aimed at
reducing the ambiguity in user utterances by leveraging user-specific
supporting profile information. However, research in these two tasks has not
been extensively explored. To fill this gap, we propose a joint model, namely
JPIS, designed to enhance profile-based intent detection and slot filling. JPIS
incorporates the supporting profile information into its encoder and introduces
a slot-to-intent attention mechanism to transfer slot information
representations to intent detection. Experimental results show that our JPIS
substantially outperforms previous profile-based models, establishing a new
state-of-the-art performance in overall accuracy on the Chinese benchmark
dataset ProSLU.

JPIS 模型利用支持的个人资料信息增强了基于个人资料的意图检测和槽位填充的性能，并引入了槽位到意图的注意机制，从而取得了超过先前模型的性能，在中文基准数据集 ProSLU 上建立了最新的最佳性能。

JPIS：面向基于配置文件的意图检测与槽位填充的联合模型及槽位到意图的注意力

JPIS: A Joint Model for Profile-based Intent Detection and Slot Filling  with Slot-to-Intent Attention

The ability to generate clarification questions i.e., questions that identify
useful missing information in a given context, is important in reducing
ambiguity. Humans use previous experience with similar contexts to form a
global view and compare it to the given context to ascertain what is missing
and what is useful in the context. Inspired by this, we propose a model for
clarification question generation where we first identify what is missing by
taking a difference between the global and the local view and then train a
model to identify what is useful and generate a question about it. Our model
outperforms several baselines as judged by both automatic metrics and humans.

提出了一个模型，用于生成在给定语境中识别有用的缺失信息的澄清问题，该模型通过学习区分全局和本地视图之间的差异，并训练模型识别有用信息并生成问题，实验证明该模型的表现优于基线模型。