no code implementations • 15 Feb 2024 • Yinhong Liu, Yimai Fang, David Vandyke, Nigel Collier
In light of recent advances in large language models (LLMs), the expectations for the next generation of virtual assistants include enhanced naturalness and adaptability across diverse usage scenarios.
no code implementations • 4 Dec 2022 • Benjamin Muller, Deepanshu Gupta, Siddharth Patwardhan, Jean-Philippe Fauconnier, David Vandyke, Sachin Agarwal
For a given language, we are able to predict zero-shot performance, that increases on a logarithmic scale with the number of few-shot target language data points.
no code implementations • CAI (COLING) 2022 • Josef Valvoda, Yimai Fang, David Vandyke
Dialog modelling faces a difficult trade-off.
2 code implementations • Findings (EMNLP) 2021 • Yixuan Su, David Vandyke, Sihui Wang, Yimai Fang, Nigel Collier
However, the lack of ability of neural models to control the structure of generated output can be limiting in certain real-world applications.
1 code implementation • EACL 2021 • Yixuan Su, Deng Cai, Yan Wang, David Vandyke, Simon Baker, Piji Li, Nigel Collier
In this work, we show that BERT can be employed as the backbone of a NAG model to greatly improve performance.
1 code implementation • ACL 2020 • Bo-Hsiang Tseng, Jianpeng Cheng, Yimai Fang, David Vandyke
This approach allows us to explore both spaces of natural language and formal representations, and facilitates information sharing through the latent space to eventually benefit NLU and NLG.
Natural Language Understanding Task-Oriented Dialogue Systems +1
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.
2 code implementations • NAACL 2016 • Nikola Mrkšić, Diarmuid Ó Séaghdha, Blaise Thomson, Milica Gašić, Lina Rojas-Barahona, Pei-Hao Su, David Vandyke, Tsung-Hsien Wen, Steve Young
In this work, we present a novel counter-fitting method which injects antonymy and synonymy constraints into vector space representations in order to improve the vectors' capability for judging semantic similarity.
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.
no code implementations • IJCNLP 2015 • Nikola Mrkšić, Diarmuid Ó Séaghdha, Blaise Thomson, Milica Gašić, Pei-Hao Su, David Vandyke, Tsung-Hsien Wen, Steve Young
Dialog state tracking is a key component of many modern dialog systems, most of which are designed with a single, well-defined domain in mind.