In task-oriented dialogue systems, intent detection is crucial for interpreting user queries and providing appropriate responses. Existing research primarily addresses simple queries with a single intent, lacking effective systems for handling complex queries with multiple intents and extracting different intent spans. Additionally, there is a notable absence of multilingual, multi-intent datasets. This study addresses three critical tasks: extracting multiple intent spans from queries, detecting multiple intents, and developing a multi-lingual multi-label intent dataset. We introduce a novel multi-label multi-class intent detection dataset (MLMCID-dataset) curated from existing benchmark datasets. We also propose a pointer network-based architecture (MLMCID) to extract intent spans and detect multiple intents with coarse and fine-grained labels in the form of sextuplets. Comprehensive analysis demonstrates the superiority of our pointer network-based system over baseline approaches in terms of accuracy and F1-score across various datasets.

翻译：在面向任务的对话系统中，意图检测对于理解用户查询并提供恰当响应至关重要。现有研究主要针对单一意图的简单查询，缺乏有效处理多意图复杂查询并抽取不同意图跨度的系统。此外，当前明显缺乏多语言、多意图的数据集。本研究致力于解决三个关键任务：从查询中抽取多个意图跨度、检测多个意图，以及构建多语言多标签意图数据集。我们提出了一个基于现有基准数据集构建的新型多标签多类别意图检测数据集（MLMCID-dataset）。同时，我们设计了一种基于指针网络的架构（MLMCID），以六元组形式抽取意图跨度并检测带有粗粒度与细粒度标签的多重意图。综合分析表明，基于指针网络的系统在多个数据集上的准确率与F1分数均优于基线方法。