  We present NLU++, a novel 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. Finally, we benchmark a series of current
state-of-the-art NLU models on NLU++; the results demonstrate the challenging
nature of the dataset, especially in low-data regimes, the validity of `intent
modularisation', and call for further research on ToD NLU.
