论文标题
AGIF：连接多重意图检测和插槽填充的自适应图形相互作用框架
AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling
论文作者
论文摘要
在实际情况下，用户通常在同一话语中具有多种意图。不幸的是，大多数口语理解（SLU）模型主要集中在单一意图方案上，或者只是为所有令牌纳入了整体意图上下文向量，而忽略了用于代币级别的插槽预测的精细元素多意信息信息集成。在本文中，我们提出了一个自适应图形相互作用框架（AGIF），用于接头多重意图检测和插槽填充，在此我们引入了一个Intent-Slot图相互作用层，以建模插槽和意图之间的强相关性。这种相互作用层适用于每个令牌，这具有自动提取相关意图信息的优势，从而为令牌级的插槽预测提供了细粒度的意图信息集成。三个多智能数据集的实验结果表明，我们的框架可获得实质性的改进并实现最先进的性能。此外，我们的框架还可以在两个单个数据集上实现新的最新性能。
In real-world scenarios, users usually have multiple intents in the same utterance. Unfortunately, most spoken language understanding (SLU) models either mainly focused on the single intent scenario, or simply incorporated an overall intent context vector for all tokens, ignoring the fine-grained multiple intents information integration for token-level slot prediction. In this paper, we propose an Adaptive Graph-Interactive Framework (AGIF) for joint multiple intent detection and slot filling, where we introduce an intent-slot graph interaction layer to model the strong correlation between the slot and intents. Such an interaction layer is applied to each token adaptively, which has the advantage to automatically extract the relevant intents information, making a fine-grained intent information integration for the token-level slot prediction. Experimental results on three multi-intent datasets show that our framework obtains substantial improvement and achieves the state-of-the-art performance. In addition, our framework achieves new state-of-the-art performance on two single-intent datasets.
