170 citations · 812 across the 18 of their papers we have counts for
28 papers
Multilingual and Cross-Lingual Intent Detection from Spoken Data
Daniela Gerz, Pei-Hao Su, Razvan Kusztos +6
We present a systematic study on multilingual and cross-lingual intent detection from spoken data. The study leverages a new resource put forth in this work, termed MInDS-14, a fir…
PolyResponse: A Rank-based Approach to Task-Oriented Dialogue with Application in Restaurant Search and Booking
Matthew Henderson, Ivan Vulić, Iñigo Casanueva +7
We present PolyResponse, a conversational search engine that supports task-oriented dialogue. It is a retrieval-based approach that bypasses the complex multi-component design of t…
Training Neural Response Selection for Task-Oriented Dialogue Systems
Matthew Henderson, Ivan Vulić, Daniela Gerz +7
Despite their popularity in the chatbot literature, retrieval-based models have had modest impact on task-oriented dialogue systems, with the main obstacle to their application bei…
A Repository of Conversational Datasets
Matthew Henderson, Paweł Budzianowski, Iñigo Casanueva +8
Progress in Machine Learning is often driven by the availability of large datasets, and consistent evaluation metrics for comparing modeling approaches. To this end, we present a r…
Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization
Edoardo Maria Ponti, Ivan Vulić, Goran Glavaš +2
Semantic specialization is the process of fine-tuning pre-trained distributional word vectors using external lexical knowledge (e.g., WordNet) to accentuate a particular semantic r…
Fully Statistical Neural Belief Tracking
Nikola Mrkšić, Ivan Vulić
This paper proposes an improvement to the existing data-driven Neural Belief Tracking (NBT) framework for Dialogue State Tracking (DST). The existing NBT model uses a hand-crafted…
Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources
Ivan Vulić, Goran Glavaš, Nikola Mrkšić +1
Word vector specialisation (also known as retrofitting) is a portable, light-weight approach to fine-tuning arbitrary distributional word vector spaces by injecting external knowle…
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…
Specialising Word Vectors for Lexical Entailment
Ivan Vulić, Nikola Mrkšić
We present LEAR (Lexical Entailment Attract-Repel), a novel post-processing method that transforms any input word vector space to emphasise the asymmetric relation of lexical entai…
Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation
Ivan Vulić, Nikola Mrkšić, Anna Korhonen
Existing approaches to automatic VerbNet-style verb classification are heavily dependent on feature engineering and therefore limited to languages with mature NLP pipelines. In thi…
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…
Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific Rules
Ivan Vulić, Nikola Mrkšić, Roi Reichart +3
Morphologically rich languages accentuate two properties of distributional vector space models: 1) the difficulty of inducing accurate representations for low-frequency word forms;…
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…
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…
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…