Spoken Dialogue Systems
18 papers with code • 0 benchmarks • 2 datasets
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Most implemented papers
Plato Dialogue System: A Flexible Conversational AI Research Platform
Plato has been designed to be easy to understand and debug and is agnostic to the underlying learning frameworks that train each component.
Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems
Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact both on usability and perceived quality.
Slot-Gated Modeling for Joint Slot Filling and Intent Prediction
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.
A Context-aware Natural Language Generator for Dialogue Systems
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.
Towards Learning Transferable Conversational Skills using Multi-dimensional Dialogue Modelling
Recent statistical approaches have improved the robustness and scalability of spoken dialogue systems.
How Time Matters: Learning Time-Decay Attention for Contextual Spoken Language Understanding in Dialogues
Spoken language understanding (SLU) is an essential component in conversational systems.
Natural Language Generation by Hierarchical Decoding with Linguistic Patterns
Natural language generation (NLG) is a critical component in spoken dialogue systems.
Findings of the E2E NLG Challenge
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.