2 citations · 4 across the 11 of their papers we have counts for
5 papers · 1 filter
Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems
Bo-Hsiang Tseng, Florian Kreyssig, Pawel Budzianowski +4
Cross-domain natural language generation (NLG) is still a difficult task within spoken dialogue modelling. Given a semantic representation provided by the dialogue manager, the lan…
MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling
Paweł Budzianowski, Tsung-Hsien Wen, Bo-Hsiang Tseng +4
Even though machine learning has become the major scene in dialogue research community, the real breakthrough has been blocked by the scale of data available. To address this funda…
Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy
Lina Rojas-Barahona, Bo-Hsiang Tseng, Yinpei Dai +5
In recent years, we have seen deep learning and distributed representations of words and sentences make impact on a number of natural language processing tasks, such as similarity,…
Nearly Zero-Shot Learning for Semantic Decoding in Spoken Dialogue Systems
Lina M. Rojas-Barahona, Stefan Ultes, Pawel Budzianowski +4
This paper presents two ways of dealing with scarce data in semantic decoding using N-Best speech recognition hypotheses. First, we learn features by using a deep learning architec…
Feudal Reinforcement Learning for Dialogue Management in Large Domains
Iñigo Casanueva, Paweł Budzianowski, Pei-Hao Su +4
Reinforcement learning (RL) is a promising approach to solve dialogue policy optimisation. Traditional RL algorithms, however, fail to scale to large domains due to the curse of di…