170 citations · 812 across the 18 of their papers we have counts for
9 papers · 1 filter
Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding
Lina M. Rojas Barahona, Milica Gasic, Nikola Mrkšić +4
This paper presents a deep learning architecture for the semantic decoder component of a Statistical Spoken Dialogue System. In a slot-filling dialogue, the semantic decoder predic…
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…
Neural Belief Tracker: Data-Driven Dialogue State Tracking
Nikola Mrkšić, Diarmuid Ó Séaghdha, Tsung-Hsien Wen +2
One of the core components of modern spoken dialogue systems is the belief tracker, which estimates the user's goal at every step of the dialogue. However, most current approaches…
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…
Multi-domain Neural Network Language Generation for Spoken Dialogue Systems
Tsung-Hsien Wen, Milica Gasic, Nikola Mrksic +4
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 do…
Counter-fitting Word Vectors to Linguistic Constraints
Nikola Mrkšić, Diarmuid Ó Séaghdha, Blaise Thomson +6
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' capabilit…