MULTI3NLU++：面向任务型对话中自然语言理解的多语言、多意图、多领域数据集
MULTI3NLU++: A Multilingual, Multi-Intent, Multi-Domain Dataset for Natural Language Understanding in Task-Oriented Dialogue
AI总结：
构建多语言多意图多领域数据集MULTI3NLU++，用于评测任务型对话系统的意图检测和槽位标注，发现低资源语言场景下性能挑战大。
摘要：
任务型对话（TOD）系统已在许多行业中被广泛部署，因为它们能提供更高效的客户支持。这些系统通常为单一领域或语言构建，并且在此之外泛化能力不佳。为了支持跨多种语言和领域同时进行的任务型对话中的自然语言理解（NLU）研究，我们构建了MULTI3NLU++，一个多语言、多意图、多领域的数据集。MULTI3NLU++扩展了仅含英语的NLU++数据集，加入了人工翻译成一系列高、中、低资源语言（西班牙语、马拉地语、土耳其语和阿姆哈拉语）的版本，涵盖两个领域（银行和酒店）。由于其多意图特性，MULTI3NLU++代表了复杂且自然的用户目标，因此使我们能够在世界各种语言中衡量TOD系统的真实性能。我们使用MULTI3NLU++来基准测试最先进的多语言模型，用于多语言环境下TOD系统的意图检测和槽位标注NLU任务。结果表明该数据集具有挑战性，尤其是在低资源语言环境下，为未来在多领域多语言TOD设置中的实验提供了充足空间。
英文摘要：
Task-oriented dialogue (TOD) systems have been widely deployed in many industries as they deliver more efficient customer support. These systems are typically constructed for a single domain or language and do not generalise well beyond this. To support work on Natural Language Understanding (NLU) in TOD across multiple languages and domains simultaneously, we constructed MULTI3NLU++, a multilingual, multi-intent, multi-domain dataset. MULTI3NLU++ extends the English only NLU++ dataset to include manual translations into a range of high, medium, and low resource languages (Spanish, Marathi, Turkish and Amharic), in two domains (BANKING and HOTELS). Because of its multi-intent property, MULTI3NLU++ represents complex and natural user goals, and therefore allows us to measure the realistic performance of TOD systems in a varied set of the world's languages. We use MULTI3NLU++ to benchmark state-of-the-art multilingual models for the NLU tasks of intent detection and slot labelling for TOD systems in the multilingual setting. The results demonstrate the challenging nature of the dataset, particularly in the low-resource language setting, offering ample room for future experimentation in multi-domain multilingual TOD setups.
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