提出新模型联合抽取多意图文本片段并识别，解决复杂对话理解难题。
A Pointer Network-based Approach for Joint Extraction and Detection of Multi-Label Multi-Class Intents
- 用指针网络同时定位多个意图的文本片段和标签
- 在多语言数据集上准确率与F1值均优于传统方法
- 构建首个支持多语言多意图标注的公开数据集
在任务导向对话系统中，意图检测对理解用户查询至关重要。现有研究主要针对单意图简单查询，缺乏有效处理含多个意图的复杂查询及意图片段提取的系统。此外，多语言、多意图数据集严重缺失。本文解决三个关键问题：从查询中提取多个意图片段、检测多个意图，并构建多语言多标签意图数据集。我们基于现有基准数据集构建了新的多标签多类别意图检测数据集（MLMCID-dataset）。还提出一种基于指针网络的架构（MLMCID），以六元组形式实现粗粒度与细粒度意图标签的联合抽取。综合分析表明，该指针网络系统在多种数据集上的准确率和F1值均显著优于基线方法。
原文摘要 · Abstract (English)
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.
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