In this work, we focus on two core tasks within the digital assistant pipeline: intent classification and slot detection. Intent classification recovers the goal of the utterance, whereas slot detection identifies important properties regarding this goal. Besides introducing a novel cross-lingual dataset for these tasks, consisting of 11 languages, we evaluate a variety of models: 1)
multilingually pretrained transformer-based models, 2) we supplement these models with auxiliary tasks to evaluate whether multi-task learning can be beneficial, and 3) annotation transfer with neural machine translation.
|Publication date||25 Sept 2021|
|Publication status||Published - 25 Sept 2021|
: RESOURCEs and representations For Under-resourced Languages and domains - Gothenburg, Gothenburg, Sweden
Duration: 25 Nov 2020 → …
|Period||25/11/2020 → …|