Steve Young

Also published as: Steven Young


2019

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Proceedings of the Second Financial Narrative Processing Workshop (FNP 2019)
Mahmoud El-Haj | Paul Rayson | Steven Young | Houda Bouamor | Sira Ferradans
Proceedings of the Second Financial Narrative Processing Workshop (FNP 2019)

2018

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Addressing Objects and Their Relations: The Conversational Entity Dialogue Model
Stefan Ultes | Paweł Budzianowski | Iñigo Casanueva | Lina M. Rojas-Barahona | Bo-Hsiang Tseng | Yen-Chen Wu | Steve Young | Milica Gašić
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

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., relations. In this work, we propose a novel dialogue model that is centred around entities and is able to model relations as well as multiple entities of the same type. We demonstrate in a prototype implementation benefits of relation modelling on the dialogue level and show that a trained policy using these relations outperforms the multi-domain baseline. Furthermore, we show that by modelling the relations on the dialogue level, the system is capable of processing relations present in the user input and even learns to address them in the system response.

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Unsupervised Identification of Study Descriptors in Toxicology Research: An Experimental Study
Drahomira Herrmannova | Steven Young | Robert Patton | Christopher Stahl | Nicole Kleinstreuer | Mary Wolfe
Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis

Identifying and extracting data elements such as study descriptors in publication full texts is a critical yet manual and labor-intensive step required in a number of tasks. In this paper we address the question of identifying data elements in an unsupervised manner. Specifically, provided a set of criteria describing specific study parameters, such as species, route of administration, and dosing regimen, we develop an unsupervised approach to identify text segments (sentences) relevant to the criteria. A binary classifier trained to identify publications that met the criteria performs better when trained on the candidate sentences than when trained on sentences randomly picked from the text, supporting the intuition that our method is able to accurately identify study descriptors.

2017

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Reward-Balancing for Statistical Spoken Dialogue Systems using Multi-objective Reinforcement Learning
Stefan Ultes | Paweł Budzianowski | Iñigo Casanueva | Nikola Mrkšić | Lina M. Rojas-Barahona | Pei-Hao Su | Tsung-Hsien Wen | Milica Gašić | Steve Young
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

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 dialogue length. In this work, we propose a structured method for finding a good balance between these components by searching for the optimal reward component weighting. To render this search feasible, we use multi-objective reinforcement learning to significantly reduce the number of training dialogues required. We apply our proposed method to find optimized component weights for six domains and compare them to a default baseline.

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Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management
Pei-Hao Su | Paweł Budzianowski | Stefan Ultes | Milica Gašić | Steve Young
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

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. This is especially problematic for on-line learning with real users. Two approaches are introduced to tackle this problem. Firstly, to speed up the learning process, two sample-efficient neural networks algorithms: trust region actor-critic with experience replay (TRACER) and episodic natural actor-critic with experience replay (eNACER) are presented. For TRACER, the trust region helps to control the learning step size and avoid catastrophic model changes. For eNACER, the natural gradient identifies the steepest ascent direction in policy space to speed up the convergence. Both models employ off-policy learning with experience replay to improve sample-efficiency. Secondly, to mitigate the cold start issue, a corpus of demonstration data is utilised to pre-train the models prior to on-line reinforcement learning. Combining these two approaches, we demonstrate a practical approach to learn deep RL-based dialogue policies and demonstrate their effectiveness in a task-oriented information seeking domain.

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DialPort, Gone Live: An Update After A Year of Development
Kyusong Lee | Tiancheng Zhao | Yulun Du | Edward Cai | Allen Lu | Eli Pincus | David Traum | Stefan Ultes | Lina M. Rojas-Barahona | Milica Gasic | Steve Young | Maxine Eskenazi
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

DialPort collects user data for connected spoken dialog systems. At present six systems are linked to a central portal that directs the user to the applicable system and suggests systems that the user may be interested in. User data has started to flow into the system.

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Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific Rules
Ivan Vulić | Nikola Mrkšić | Roi Reichart | Diarmuid Ó Séaghdha | Steve Young | Anna Korhonen
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Morphologically rich languages accentuate two properties of distributional vector space models: 1) the difficulty of inducing accurate representations for low-frequency word forms; and 2) insensitivity to distinct lexical relations that have similar distributional signatures. These effects are detrimental for language understanding systems, which may infer that ‘inexpensive’ is a rephrasing for ‘expensive’ or may not associate ‘acquire’ with ‘acquires’. In this work, we propose a novel morph-fitting procedure which moves past the use of curated semantic lexicons for improving distributional vector spaces. Instead, our method injects morphological constraints generated using simple language-specific rules, pulling inflectional forms of the same word close together and pushing derivational antonyms far apart. In intrinsic evaluation over four languages, we show that our approach: 1) improves low-frequency word estimates; and 2) boosts the semantic quality of the entire word vector collection. Finally, we show that morph-fitted vectors yield large gains in the downstream task of dialogue state tracking, highlighting the importance of morphology for tackling long-tail phenomena in language understanding tasks.

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Neural Belief Tracker: Data-Driven Dialogue State Tracking
Nikola Mrkšić | Diarmuid Ó Séaghdha | Tsung-Hsien Wen | Blaise Thomson | Steve Young
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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 have difficulty scaling to larger, more complex dialogue domains. This is due to their dependency on either: a) Spoken Language Understanding models that require large amounts of annotated training data; or b) hand-crafted lexicons for capturing some of the linguistic variation in users’ language. We propose a novel Neural Belief Tracking (NBT) framework which overcomes these problems by building on recent advances in representation learning. NBT models reason over pre-trained word vectors, learning to compose them into distributed representations of user utterances and dialogue context. Our evaluation on two datasets shows that this approach surpasses past limitations, matching the performance of state-of-the-art models which rely on hand-crafted semantic lexicons and outperforming them when such lexicons are not provided.

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PyDial: A Multi-domain Statistical Dialogue System Toolkit
Stefan Ultes | Lina M. Rojas-Barahona | Pei-Hao Su | David Vandyke | Dongho Kim | Iñigo Casanueva | Paweł Budzianowski | Nikola Mrkšić | Tsung-Hsien Wen | Milica Gašić | Steve Young
Proceedings of ACL 2017, System Demonstrations

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Semantic Specialization of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints
Nikola Mrkšić | Ivan Vulić | Diarmuid Ó Séaghdha | Ira Leviant | Roi Reichart | Milica Gašić | Anna Korhonen | Steve Young
Transactions of the Association for Computational Linguistics, Volume 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 use of constraints from mono- and cross-lingual resources, yielding semantically specialized cross-lingual vector spaces. Our evaluation shows that the method can make use of existing cross-lingual lexicons to construct high-quality vector spaces for a plethora of different languages, facilitating semantic transfer from high- to lower-resource ones. The effectiveness of our approach is demonstrated with state-of-the-art results on semantic similarity datasets in six languages. We next show that Attract-Repel-specialized vectors boost performance in the downstream task of dialogue state tracking (DST) across multiple languages. Finally, we show that cross-lingual vector spaces produced by our algorithm facilitate the training of multilingual DST models, which brings further performance improvements.

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A Network-based End-to-End Trainable Task-oriented Dialogue System
Tsung-Hsien Wen | David Vandyke | Nikola Mrkšić | Milica Gašić | Lina M. Rojas-Barahona | Pei-Hao Su | Stefan Ultes | Steve Young
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Teaching machines to accomplish tasks by conversing naturally with humans is challenging. Currently, developing task-oriented dialogue systems requires creating multiple components and typically this involves either a large amount of handcrafting, or acquiring costly labelled datasets to solve a statistical learning problem for each component. In this work we introduce a neural network-based text-in, text-out end-to-end trainable goal-oriented dialogue system along with a new way of collecting dialogue data based on a novel pipe-lined Wizard-of-Oz framework. This approach allows us to develop dialogue systems easily and without making too many assumptions about the task at hand. The results show that the model can converse with human subjects naturally whilst helping them to accomplish tasks in a restaurant search domain.

2016

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Conditional Generation and Snapshot Learning in Neural Dialogue Systems
Tsung-Hsien Wen | Milica Gašić | Nikola Mrkšić | Lina M. Rojas-Barahona | Pei-Hao Su | Stefan Ultes | David Vandyke | Steve Young
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems
Pei-Hao Su | Milica Gašić | Nikola Mrkšić | Lina M. Rojas-Barahona | Stefan Ultes | David Vandyke | Tsung-Hsien Wen | Steve Young
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Learning Tone and Attribution for Financial Text Mining
Mahmoud El-Haj | Paul Rayson | Steve Young | Andrew Moore | Martin Walker | Thomas Schleicher | Vasiliki Athanasakou
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Attribution bias refers to the tendency of people to attribute successes to their own abilities but failures to external factors. In a business context an internal factor might be the restructuring of the firm and an external factor might be an unfavourable change in exchange or interest rates. In accounting research, the presence of an attribution bias has been demonstrated for the narrative sections of the annual financial reports. Previous studies have applied manual content analysis to this problem but in this paper we present novel work to automate the analysis of attribution bias through using machine learning algorithms. Previous studies have only applied manual content analysis on a small scale to reveal such a bias in the narrative section of annual financial reports. In our work a group of experts in accounting and finance labelled and annotated a list of 32,449 sentences from a random sample of UK Preliminary Earning Announcements (PEAs) to allow us to examine whether sentences in PEAs contain internal or external attribution and which kinds of attributions are linked to positive or negative performance. We wished to examine whether human annotators could agree on coding this difficult task and whether Machine Learning (ML) could be applied reliably to replicate the coding process on a much larger scale. Our best machine learning algorithm correctly classified performance sentences with 70% accuracy and detected tone and attribution in financial PEAs with accuracy of 79%.

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Towards Using Conversations with Spoken Dialogue Systems in the Automated Assessment of Non-Native Speakers of English
Diane Litman | Steve Young | Mark Gales | Kate Knill | Karen Ottewell | Rogier van Dalen | David Vandyke
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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Multi-domain Neural Network Language Generation for Spoken Dialogue Systems
Tsung-Hsien Wen | Milica Gašić | Nikola Mrkšić | Lina M. Rojas-Barahona | Pei-Hao Su | David Vandyke | Steve Young
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Counter-fitting Word Vectors to Linguistic Constraints
Nikola Mrkšić | Diarmuid Ó Séaghdha | Blaise Thomson | Milica Gašić | Lina M. Rojas-Barahona | Pei-Hao Su | David Vandyke | Tsung-Hsien Wen | Steve Young
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding
Lina M. Rojas-Barahona | Milica Gašić | Nikola Mrkšić | Pei-Hao Su | Stefan Ultes | Tsung-Hsien Wen | Steve Young
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

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 predicts the dialogue act and a set of slot-value pairs from a set of n-best hypotheses returned by the Automatic Speech Recognition. Most current models for spoken language understanding assume (i) word-aligned semantic annotations as in sequence taggers and (ii) delexicalisation, or a mapping of input words to domain-specific concepts using heuristics that try to capture morphological variation but that do not scale to other domains nor to language variation (e.g., morphology, synonyms, paraphrasing ). In this work the semantic decoder is trained using unaligned semantic annotations and it uses distributed semantic representation learning to overcome the limitations of explicit delexicalisation. The proposed architecture uses a convolutional neural network for the sentence representation and a long-short term memory network for the context representation. Results are presented for the publicly available DSTC2 corpus and an In-car corpus which is similar to DSTC2 but has a significantly higher word error rate (WER).

2015

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Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems
Tsung-Hsien Wen | Milica Gašić | Nikola Mrkšić | Pei-Hao Su | David Vandyke | Steve Young
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking
Tsung-Hsien Wen | Milica Gašić | Dongho Kim | Nikola Mrkšić | Pei-Hao Su | David Vandyke | Steve Young
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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Reward Shaping with Recurrent Neural Networks for Speeding up On-Line Policy Learning in Spoken Dialogue Systems
Pei-Hao Su | David Vandyke | Milica Gašić | Nikola Mrkšić | Tsung-Hsien Wen | Steve Young
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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Multi-domain Dialog State Tracking using Recurrent Neural Networks
Nikola Mrkšić | Diarmuid Ó Séaghdha | Blaise Thomson | Milica Gašić | Pei-Hao Su | David Vandyke | Tsung-Hsien Wen | Steve Young
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2014

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Stochastic Language Generation in Dialogue using Factored Language Models
François Mairesse | Steve Young
Computational Linguistics, Volume 40, Issue 4 - December 2014

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Keynote: Statistical Approaches to Open-domain Spoken Dialogue Systems
Steve Young
Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

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The PARLANCE mobile application for interactive search in English and Mandarin
Helen Hastie | Marie-Aude Aufaure | Panos Alexopoulos | Hugues Bouchard | Catherine Breslin | Heriberto Cuayáhuitl | Nina Dethlefs | Milica Gašić | James Henderson | Oliver Lemon | Xingkun Liu | Peter Mika | Nesrine Ben Mustapha | Tim Potter | Verena Rieser | Blaise Thomson | Pirros Tsiakoulis | Yves Vanrompay | Boris Villazon-Terrazas | Majid Yazdani | Steve Young | Yanchao Yu
Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

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Word-Based Dialog State Tracking with Recurrent Neural Networks
Matthew Henderson | Blaise Thomson | Steve Young
Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

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Detecting Document Structure in a Very Large Corpus of UK Financial Reports
Mahmoud El-Haj | Paul Rayson | Steve Young | Martin Walker
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this paper we present the evaluation of our automatic methods for detecting and extracting document structure in annual financial reports. The work presented is part of the Corporate Financial Information Environment (CFIE) project in which we are using Natural Language Processing (NLP) techniques to study the causes and consequences of corporate disclosure and financial reporting outcomes. We aim to uncover the determinants of financial reporting quality and the factors that influence the quality of information disclosed to investors beyond the financial statements. The CFIE consists of the supply of information by firms to investors, and the mediating influences of information intermediaries on the timing, relevance and reliability of information available to investors. It is important to compare and contrast specific elements or sections of each annual financial report across our entire corpus rather than working at the full document level. We show that the values of some metrics e.g. readability will vary across sections, thus improving on previous research research based on full texts.

2013

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POMDP-based dialogue manager adaptation to extended domains
Milica Gašić | Catherine Breslin | Matthew Henderson | Dongho Kim | Martin Szummer | Blaise Thomson | Pirros Tsiakoulis | Steve Young
Proceedings of the SIGDIAL 2013 Conference

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Deep Neural Network Approach for the Dialog State Tracking Challenge
Matthew Henderson | Blaise Thomson | Steve Young
Proceedings of the SIGDIAL 2013 Conference

2012

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The Effect of Cognitive Load on a Statistical Dialogue System
Milica Gašić | Pirros Tsiakoulis | Matthew Henderson | Blaise Thomson | Kai Yu | Eli Tzirkel | Steve Young
Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue

2011

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Spoken Dialog Challenge 2010: Comparison of Live and Control Test Results
Alan W Black | Susanne Burger | Alistair Conkie | Helen Hastie | Simon Keizer | Oliver Lemon | Nicolas Merigaud | Gabriel Parent | Gabriel Schubiner | Blaise Thomson | Jason D. Williams | Kai Yu | Steve Young | Maxine Eskenazi
Proceedings of the SIGDIAL 2011 Conference

2010

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Parameter estimation for agenda-based user simulation
Simon Keizer | Milica Gašić | Filip Jurčíček | François Mairesse | Blaise Thomson | Kai Yu | Steve Young
Proceedings of the SIGDIAL 2010 Conference

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Gaussian Processes for Fast Policy Optimisation of POMDP-based Dialogue Managers
Milica Gašić | Filip Jurčíček | Simon Keizer | Francois Mairesse | Blaise Thomson | Kai Yu | Steve Young
Proceedings of the SIGDIAL 2010 Conference

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Phrase-Based Statistical Language Generation Using Graphical Models and Active Learning
François Mairesse | Milica Gašić | Filip Jurčíček | Simon Keizer | Blaise Thomson | Kai Yu | Steve Young
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

2009

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Proceedings of the SIGDIAL 2009 Conference
Patrick Healey | Roberto Pieraccini | Donna Byron | Steve Young | Matthew Purver
Proceedings of the SIGDIAL 2009 Conference

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k-Nearest Neighbor Monte-Carlo Control Algorithm for POMDP-Based Dialogue Systems
Fabrice Lefèvre | Milica Gašić | Filip Jurčíček | Simon Keizer | François Mairesse | Blaise Thomson | Kai Yu | Steve Young
Proceedings of the SIGDIAL 2009 Conference

2008

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Training and Evaluation of the HIS POMDP Dialogue System in Noise
Milica Gašić | Simon Keizer | Francois Mairesse | Jost Schatzmann | Blaise Thomson | Kai Yu | Steve Young
Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue

2007

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Agenda-Based User Simulation for Bootstrapping a POMDP Dialogue System
Jost Schatzmann | Blaise Thomson | Karl Weilhammer | Hui Ye | Steve Young
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers

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The Hidden Information State Dialogue Manager: A Real-World POMDP-Based System
Steve Young | Jost Schatzmann | Blaise Thomson | Karl Weilhammer | Hui Ye
Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT)

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Statistical User Simulation with a Hidden Agenda
Jost Schatzmann | Blaise Thomson | Steve Young
Proceedings of the 8th SIGdial Workshop on Discourse and Dialogue

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Training a real-world POMDP-based Dialog System
Blaise Thomson | Jost Schatzmann | Karl Weilhammer | Hui Ye | Steve Young
Proceedings of the Workshop on Bridging the Gap: Academic and Industrial Research in Dialog Technologies

2005

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Partially Observable Markov Decision Processes with Continuous Observations for Dialogue Management
Jason D. Williams | Pascal Poupart | Steve Young
Proceedings of the 6th SIGdial Workshop on Discourse and Dialogue

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Quantitative Evaluation of User Simulation Techniques for Spoken Dialogue Systems
Jost Schatzmann | Kallirroi Georgila | Steve Young
Proceedings of the 6th SIGdial Workshop on Discourse and Dialogue

2004

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Robustness Issues in a Data-Driven Spoken Language Understanding System
Yulan He | Steve Young
Proceedings of the HLT-NAACL 2004 Workshop on Spoken Language Understanding for Conversational Systems and Higher Level Linguistic Information for Speech Processing

2003

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Using Wizard-of-Oz simulations to bootstrap Reinforcement - Learning based dialog management systems
Jason D. Williams | Steve Young
Proceedings of the Fourth SIGdial Workshop of Discourse and Dialogue

1997

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A Study on the Portability of a Grammatical Inference System
Hsue-Hueh Shih | Steve Young
ROCLING 1997 Poster Papers

1994

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Session 11: Acoustic Modeling and Robust CSR
Steve Young
Human Language Technology: Proceedings of a Workshop held at Plainsboro, New Jersey, March 8-11, 1994

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