Imankulova, Aizhan

Khairunnisa, Siti Oryza

Komachi, Mamoru

Plank, Barbara

Ramponi, Alan

Sharaf, Ibrahim

Stepanovic, Marija

van der Goot, Rob

Üstün, Ahmet

Digital assistants are becoming an integral part of everyday life. However, commercial digital assistants are only available for a limited set of languages. Because of this, a vast amount of people can not use these devices in their native tongue.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

The IT University of Copenhagen's Repository

Cross-lingual Multi-task Transfer for Zero-shot Task-oriented Dialog

Cross-lingual Multi-task Transfer for Zero-shot Task-oriented Dialog

Abstract

Similar works

Full text

Available Versions

The IT University of Copenhagen's Repository