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English. We present the FBK participation at the EVALITA 2018 Shared Task “SUGAR – Spoken Utterances Guiding Chef’s Assistant Robots”. There are two peculiar, and challenging, characteristics of the task: first, the amount of available training data is very limited; second, training consists of pairs [audio-utterance, system-action], without any intermediate representation. Given the characteristics of the task, we experimented two different approaches: (i) design and implement a neural architecture that can use as less training data as possible, and (ii) use a state of art tagging system, and then augment the initial training set with synthetically generated data. In the paper we present the two approaches, and show the results obtained by their respective runs. Italiano. Presentiamo la partecipazione di FBK allo shared task “SUGAR – Spoken Utterances Guiding Chef’s Assistant Robots” a EVALITA 2018. Ci sono due caratteristiche peculiari del task: primo, la quantitá di dati di training é molto limitata; secondo, il training consiste di coppie [enunciato-audio, azione-sistema], senza alcuna rappresentazione intermedia. Date le caratteristiche del task, abbiamo sperimentato due approcci diversi: (i) la progettazione e implementazione di una architettura neurale che riesca ad usare la minor quantitá di traning possibile; (ii) l’uso di un sistama di tagging allo stato dell’arte, aumentato con dati generati in modo sintetico. Nel contributo presentiamo i due approcci, e mostriamo i risultati ottenuti nei loro rispettivi run.

The Perfect Recipe: Add SUGAR, Add Data