We present a new neural architecture for wide-coverage Natural Language Understanding in Spoken Dialogue Systems.
Attention-based recurrent neural network models for joint intent detection and slot filling have achieved the state-of-the-art performance, while they have independent attention weights.
This paper summarises the experimental setup and results of the first shared task on end-to-end (E2E) natural language generation (NLG) in spoken dialogue systems.
#3 best model for Data-to-Text Generation on E2E NLG Challenge
We present a novel natural language generation system for spoken dialogue systems capable of entraining (adapting) to users' way of speaking, providing contextually appropriate responses.
Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact both on usability and perceived quality.
Spoken language understanding (SLU) is an essential component in conversational systems.
Cross-domain natural language generation (NLG) is still a difficult task within spoken dialogue modelling.