NLU++ (NLLU++ : A Multi-Label, Slot-Rich, Generalisable Dataset for Natural Language Understanding in Task-Oriented Dialogue)

nlu++ is a dataset for natural language understanding (NLU) in task-oriented dialogue (ToD) systems, with the aim to provide a much more challenging evaluation environment for dialogue NLU models, up to date with the current application and industry requirements. nlu++ is divided into two domains (banking and hotels) and brings several crucial improvements over current commonly used NLU datasets. 1) Nlu++ provides fine-grained domain ontologies with a large set of challenging multi-intent sentences, introducing and validating the idea of intent modules that can be combined into complex intents that convey complex user goals, combined with finer-grained and thus more challenging slot sets. 2) The ontology is divided into domain-specific and generic (i.e., domain-universal) intent modules that overlap across domains, promoting cross-domain reusability of annotated examples. 3) The dataset design has been inspired by the problems observed in industrial ToD systems, and 4) it has been collected, filtered and carefully annotated by dialogue NLU experts, yielding high-quality annotated data.

List of datasets:

Banking: online banking queries annotated with their corresponding intents. Span Extraction: the data used for the SpanConvert paper. NLU++: a challenging evaluation environment for dialogue NLU models (multi-domain, multi-label intents and slots). EVI: a challenging multilingual dataset for knowledge-based enrollment, identification, and identification in spoken dialogue systems.