no code implementations • 9 Sep 2016 • Milica Gasic, Nikola Mrksic, Lina M. Rojas-Barahona, Pei-Hao Su, Stefan Ultes, David Vandyke, Tsung-Hsien Wen, Steve Young
Spoken dialogue systems allow humans to interact with machines using natural speech.
no code implementations • EMNLP 2016 • Tsung-Hsien Wen, Milica Gasic, Nikola Mrksic, Lina M. Rojas-Barahona, Pei-Hao Su, Stefan Ultes, David Vandyke, Steve Young
Recently a variety of LSTM-based conditional language models (LM) have been applied across a range of language generation tasks.
no code implementations • 8 Jun 2016 • Pei-Hao Su, Milica Gasic, Nikola Mrksic, Lina Rojas-Barahona, Stefan Ultes, David Vandyke, Tsung-Hsien Wen, Steve Young
We describe a two-step approach for dialogue management in task-oriented spoken dialogue systems.
no code implementations • ACL 2016 • Pei-Hao Su, Milica Gasic, Nikola Mrksic, Lina Rojas-Barahona, Stefan Ultes, David Vandyke, Tsung-Hsien Wen, Steve Young
The ability to compute an accurate reward function is essential for optimising a dialogue policy via reinforcement learning.
1 code implementation • EACL 2017 • Tsung-Hsien Wen, David Vandyke, Nikola Mrksic, Milica Gasic, Lina M. Rojas-Barahona, Pei-Hao Su, Stefan Ultes, Steve Young
Teaching machines to accomplish tasks by conversing naturally with humans is challenging.
no code implementations • NAACL 2016 • Tsung-Hsien Wen, Milica Gasic, Nikola Mrksic, Lina M. Rojas-Barahona, Pei-Hao Su, David Vandyke, Steve Young
Moving from limited-domain natural language generation (NLG) to open domain is difficult because the number of semantic input combinations grows exponentially with the number of domains.
no code implementations • WS 2015 • Pei-Hao Su, David Vandyke, Milica Gasic, Nikola Mrksic, Tsung-Hsien Wen, Steve Young
Reward shaping is one promising technique for addressing these concerns.
no code implementations • 13 Aug 2015 • Pei-Hao Su, David Vandyke, Milica Gasic, Dongho Kim, Nikola Mrksic, Tsung-Hsien Wen, Steve Young
The models are trained on dialogues generated by a simulated user and the best model is then used to train a policy on-line which is shown to perform at least as well as a baseline system using prior knowledge of the user's task.
no code implementations • WS 2015 • Tsung-Hsien Wen, Milica Gasic, Dongho Kim, Nikola Mrksic, Pei-Hao Su, David Vandyke, Steve Young
The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled dataset to be trained on.
2 code implementations • EMNLP 2015 • Tsung-Hsien Wen, Milica Gasic, Nikola Mrksic, Pei-Hao Su, David Vandyke, Steve Young
Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact both on usability and perceived quality.