一种用于联合多意图检测和槽填充的生成模型
A Generative Model for Joint Multiple Intent Detection and Slot Filling
AI总结：
本文提出一种生成模型，同时解决多意图检测和槽填充问题，通过引入归纳偏置的注意力解码器，并构建新的多意图SLU数据集，实现在公开数据集上的最佳性能。
摘要：
在以任务为导向的对话系统中，口语语言理解（SLU）是一个关键组成部分，它由两个子任务组成，即意图检测和槽填充。现有大多数方法专注于单意图SLU，每个语句只有一个意图。然而，在现实场景中，用户通常在一个语句中表达多个意图，这给现有的对话系统和数据集带来了挑战。在本文中，我们提出了一种生成框架，同时解决多意图检测和槽填充问题。特别是，提出了一种注意力-注意力解码器，通过在多任务学习过程中引入归纳偏置，来处理意图数量的可变性和两个子任务之间的干扰。此外，我们基于单意图语句构建了两个新的多意图SLU数据集，利用BERT模型的下一句预测（NSP）头。实验结果表明，我们提出的注意力-注意力生成模型在两个公开数据集MixATIS和MixSNIPS以及我们构建的数据集上均取得了最先进的性能。
英文摘要：
In task-oriented dialogue systems, spoken language understanding (SLU) is a critical component, which consists of two sub-tasks, intent detection and slot filling. Most existing methods focus on the single-intent SLU, where each utterance only has one intent. However, in real-world scenarios users usually express multiple intents in an utterance, which poses a challenge for existing dialogue systems and datasets. In this paper, we propose a generative framework to simultaneously address multiple intent detection and slot filling. In particular, an attention-over-attention decoder is proposed to handle the variable number of intents and the interference between the two sub-tasks by incorporating an inductive bias into the process of multi-task learning. Besides, we construct two new multi-intent SLU datasets based on single-intent utterances by taking advantage of the next sentence prediction (NSP) head of the BERT model. Experimental results demonstrate that our proposed attention-over-attention generative model achieves state-of-the-art performance on two public datasets, MixATIS and MixSNIPS, and our constructed datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
