124 citations · 399 across the 12 of their papers we have counts for
14 papers
Report from the NSF Future Directions Workshop on Automatic Evaluation of Dialog: Research Directions and Challenges
Shikib Mehri, Jinho Choi, Luis Fernando D'Haro +13
This is a report on the NSF Future Directions Workshop on Automatic Evaluation of Dialog. The workshop explored the current state of the art along with its limitations and suggeste…
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…
Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing
Osman Ramadan, Paweł Budzianowski, Milica Gašić
Robust dialogue belief tracking is a key component in maintaining good quality dialogue systems. The tasks that dialogue systems are trying to solve are becoming increasingly compl…
Neural User Simulation for Corpus-based Policy Optimisation for Spoken Dialogue Systems
Florian Kreyssig, Inigo Casanueva, Pawel Budzianowski +1
User Simulators are one of the major tools that enable offline training of task-oriented dialogue systems. For this task the Agenda-Based User Simulator (ABUS) is often used. The A…
Uncertainty Estimates for Efficient Neural Network-based Dialogue Policy Optimisation
Christopher Tegho, Paweł Budzianowski, Milica Gašić
In statistical dialogue management, the dialogue manager learns a policy that maps a belief state to an action for the system to perform. Efficient exploration is key to successful…
A Benchmarking Environment for Reinforcement Learning Based Task Oriented Dialogue Management
Iñigo Casanueva, Paweł Budzianowski, Pei-Hao Su +6
Dialogue assistants are rapidly becoming an indispensable daily aid. To avoid the significant effort needed to hand-craft the required dialogue flow, the Dialogue Management (DM) m…
Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management
Pei-Hao Su, Pawel Budzianowski, Stefan Ultes +2
Deep reinforcement learning (RL) methods have significant potential for dialogue policy optimisation. However, they suffer from a poor performance in the early stages of learning.…
Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints
Nikola Mrkšić, Ivan Vulić, Diarmuid Ó Séaghdha +5
We present Attract-Repel, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources. Attract-Repel facilitates the u…
Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems
Tsung-Hsien Wen, Milica Gasic, Nikola Mrksic +3
Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact both on usability and perceived quality. Most NLG systems in common use…
Reward Shaping with Recurrent Neural Networks for Speeding up On-Line Policy Learning in Spoken Dialogue Systems
Pei-Hao Su, David Vandyke, Milica Gasic +3
Statistical spoken dialogue systems have the attractive property of being able to be optimised from data via interactions with real users. However in the reinforcement learning par…
Learning from Real Users: Rating Dialogue Success with Neural Networks for Reinforcement Learning in Spoken Dialogue Systems
Pei-Hao Su, David Vandyke, Milica Gasic +4
To train a statistical spoken dialogue system (SDS) it is essential that an accurate method for measuring task success is available. To date training has relied on presenting a tas…
Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking
Tsung-Hsien Wen, Milica Gasic, Dongho Kim +4
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. The…
Multi-domain Dialog State Tracking using Recurrent Neural Networks
Nikola Mrkšić, Diarmuid Ó Séaghdha, Blaise Thomson +5
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. This paper shows that dialog data dra…