  Slot-filling, Translation, Intent classification, and Language
identification, or STIL, is a newly-proposed task for multilingual Natural
Language Understanding (NLU). By performing simultaneous slot filling and
translation into a single output language (English in this case), some portion
of downstream system components can be monolingual, reducing development and
maintenance cost. Results are given using the multilingual BART model (Liu et
al., 2020) fine-tuned on 7 languages using the MultiATIS++ dataset. When no
translation is performed, mBART's performance is comparable to the current
state of the art system (Cross-Lingual BERT by Xu et al. (2020)) for the
languages tested, with better average intent classification accuracy (96.07%
versus 95.50%) but worse average slot F1 (89.87% versus 90.81%). When
simultaneous translation is performed, average intent classification accuracy
degrades by only 1.7% relative and average slot F1 degrades by only 1.2%
relative.
