Sequence-to-sequence deep learning has recently emerged as a new paradigm in supervised learning for spoken language understanding. However, most of the previous studies explored this framework for building single domain models for each task, such as slot filling or domain classification, comparing deep learning based approaches with conventional ones like conditional random fields.
This project focuses on a holistic multi-domain, multi-task (i.e. slot filling, domain and intent detection) modeling approach to estimate complete semantic frames (domain, intent and slots) for all user utterances addressed to a conversational system, demonstrating the distinctive power of deep learning methods to handle such complexity.
This project proposes a single RNN-based architecture for slot filling, intent determination, and domain classification in a joint fashion.