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PyDial: A Multi-domain Statistical Dialogue System Toolkit

TLDR
PyDial is an opensource end-to-end statistical spoken dialogue system toolkit which provides implementations of statistical approaches for all dialogue system modules and has been extended to provide multidomain conversational functionality.
Abstract
Statistical Spoken Dialogue Systems have been around for many years. However, access to these systems has always been difficult as there is still no publicly available end-to-end system implementation. To alleviate this, we present PyDial, an opensource end-to-end statistical spoken dialogue system toolkit which provides implementations of statistical approaches for all dialogue system modules. Moreover, it has been extended to provide multidomain conversational functionality. It offers easy configuration, easy extensibility, and domain-independent implementations of the respective dialogue system modules. The toolkit is available for download under the Apache 2.0 license.

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Neural Approaches to Conversational AI

TL;DR: In this article, the authors present a survey of state-of-the-art neural approaches to conversational AI, and discuss the progress that has been made and challenges still being faced, using specific systems and models as case studies.
Proceedings ArticleDOI

Neural Approaches to Conversational AI

TL;DR: This tutorial surveys neural approaches to conversational AI that were developed in the last few years, and presents a review of state-of-the-art neural approaches, drawing the connection between neural approaches and traditional symbolic approaches.
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Rasa: Open Source Language Understanding and Dialogue Management

TL;DR: A pair of tools, Rasa NLU and Rasa Core, which are open source python libraries for building conversational software, are introduced to make machine-learning based dialogue management and language understanding accessible to non-specialist software developers.
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Building a Conversational Agent Overnight with Dialogue Self-Play

TL;DR: A new corpus of 3,000 dialogues spanning 2 domains collected with M2M is proposed, and comparisons with popular dialogue datasets on the quality and diversity of the surface forms and dialogue flows are presented.
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Bootstrapping a Neural Conversational Agent with Dialogue Self-Play, Crowdsourcing and On-Line Reinforcement Learning

TL;DR: This paper discusses the advantages of this approach for industry applications of conversational agents, wherein an agent can be rapidly bootstrapped to deploy in front of users and further optimized via interactive learning from actual users of the system.
References
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Book

Speech and Language Processing

Dan Jurafsky, +1 more
TL;DR: It is now clear that HAL's creator, Arthur C. Clarke, was a little optimistic in predicting when an artificial agent such as HAL would be avail-able as discussed by the authors.
Journal ArticleDOI

POMDP-Based Statistical Spoken Dialog Systems: A Review

TL;DR: This review article provides an overview of the current state of the art in the development of POMDP-based spoken dialog systems.
Proceedings ArticleDOI

A Network-based End-to-End Trainable Task-oriented Dialogue System

TL;DR: The authors introduced 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.
Proceedings ArticleDOI

Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems

TL;DR: A statistical language generator based on a semantically controlled Long Short-term Memory (LSTM) structure that can learn from unaligned data by jointly optimising sentence planning and surface realisation using a simple cross entropy training criterion, and language variation can be easily achieved by sampling from output candidates.
Proceedings ArticleDOI

The Second Dialog State Tracking Challenge

TL;DR: The results suggest that while large improvements on a competitive baseline are possible, trackers are still prone to degradation in mismatched conditions and ensemble learning demonstrates the most accurate tracking can be achieved by combining multiple trackers.
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