170 citations · 471 across the 19 of their papers we have counts for
17 papers · 1 filter
PolyAlign: Conditional Human-Distribution Alignment
L. D. M. S. Sai Teja, Ufaq Khan, Sathira Silva +2
Post-training methods such as supervised fine-tuning (SFT) and preference optimization typically align language models toward a single global assistant behavior. While effective fo…
Natural Language Understanding for Argumentative Dialogue Systems in the Opinion Building Domain
Waheed Ahmed Abro, Annalena Aicher, Niklas Rach +3
This paper introduces a natural language understanding (NLU) framework for argumentative dialogue systems in the information-seeking and opinion building domain. The proposed frame…
Improving Interaction Quality Estimation with BiLSTMs and the Impact on Dialogue Policy Learning
Stefan Ultes
Learning suitable and well-performing dialogue behaviour in statistical spoken dialogue systems has been in the focus of research for many years. While most work which is based on…
Addressing Objects and Their Relations: The Conversational Entity Dialogue Model
Stefan Ultes, Paweł Budzianowski, Iñigo Casanueva +5
Statistical spoken dialogue systems usually rely on a single- or multi-domain dialogue model that is restricted in its capabilities of modelling complex dialogue structures, e.g.,…
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…
Reward-Balancing for Statistical Spoken Dialogue Systems using Multi-objective Reinforcement Learning
Stefan Ultes, Paweł Budzianowski, Iñigo Casanueva +6
Reinforcement learning is widely used for dialogue policy optimization where the reward function often consists of more than one component, e.g., the dialogue success and the dialo…
Sub-domain Modelling for Dialogue Management with Hierarchical Reinforcement Learning
Paweł Budzianowski, Stefan Ultes, Pei-Hao Su +5
Human conversation is inherently complex, often spanning many different topics/domains. This makes policy learning for dialogue systems very challenging. Standard flat reinforcemen…
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.…
Dialogue manager domain adaptation using Gaussian process reinforcement learning
Milica Gasic, Nikola Mrksic, Lina M. Rojas-Barahona +5
Spoken dialogue systems allow humans to interact with machines using natural speech. As such, they have many benefits. By using speech as the primary communication medium, a comput…
Conditional Generation and Snapshot Learning in Neural Dialogue Systems
Tsung-Hsien Wen, Milica Gasic, Nikola Mrksic +5
Recently a variety of LSTM-based conditional language models (LM) have been applied across a range of language generation tasks. In this work we study various model architectures a…
Continuously Learning Neural Dialogue Management
Pei-Hao Su, Milica Gasic, Nikola Mrksic +5
We describe a two-step approach for dialogue management in task-oriented spoken dialogue systems. A unified neural network framework is proposed to enable the system to first learn…
On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems
Pei-Hao Su, Milica Gasic, Nikola Mrksic +5
The ability to compute an accurate reward function is essential for optimising a dialogue policy via reinforcement learning. In real-world applications, using explicit user feedbac…
A Network-based End-to-End Trainable Task-oriented Dialogue System
Tsung-Hsien Wen, David Vandyke, Nikola Mrksic +5
Teaching machines to accomplish tasks by conversing naturally with humans is challenging. Currently, developing task-oriented dialogue systems requires creating multiple components…