Search Results for author:
Found 4 papers, 4 papers with code
One Model, Many Languages: Meta-learning for Multilingual Text-to-Speech
We introduce an approach to multilingual speech synthesis which uses the meta-learning concept of contextual parameter generation and produces natural-sounding multilingual speech using more languages and less training data than previous approaches.
Shades of BLEU, Flavours of Success: The Case of MultiWOZ
The MultiWOZ dataset (Budzianowski et al., 2018) is frequently used for benchmarking context-to-response abilities of task-oriented dialogue systems.
AuGPT: Auxiliary Tasks and Data Augmentation for End-To-End Dialogue with Pre-Trained Language Models
Our model substantially outperforms the baseline on the MultiWOZ data and shows competitive performance with state of the art in both automatic and human evaluation.
Ranked #3 on
End-To-End Dialogue Modelling
on MULTIWOZ 2.0
(using extra training data)
AARGH! End-to-end Retrieval-Generation for Task-Oriented Dialog
We introduce AARGH, an end-to-end task-oriented dialog system combining retrieval and generative approaches in a single model, aiming at improving dialog management and lexical diversity of outputs.
