CAiRE: An End-to-End Empathetic Chatbot

We present CAiRE, an end-to-end generative empathetic chatbot designed to recognize user emotions and respond in an empathetic manner. Our system adapts the Generative Pre-trained Transformer (GPT) to empathetic response generation task via transfer learning. CAiRE is built primarily to focus on empathy integration in fully data-driven generative dialogue systems. We create a web-based user interface which allows multiple users to asynchronously chat with CAiRE. CAiRE also collects user feedback and continues to improve its response quality by discarding undesirable generations via active learning and negative training.

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End-to-end optimization of goal-driven and visually grounded dialogue systems

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/385 ◽

2017 ◽

Cited By ~ 16

Author(s):

Florian Strub ◽

Harm de Vries ◽

Jérémie Mary ◽

Bilal Piot ◽

Aaron Courville ◽

...

Keyword(s):

Gradient Algorithm ◽

Planning Problem ◽

Dialogue Systems ◽

Generation Task ◽

Policy Gradient ◽

Specific Object ◽

Complex Picture ◽

History Of ◽

End To End ◽

Task Oriented

End-to-end design of dialogue systems has recently become a popular research topic thanks to powerful tools such as encoder-decoder architectures for sequence-to-sequence learning. Yet, most current approaches cast human-machine dialogue management as a supervised learning problem, aiming at predicting the next utterance of a participant given the full history of the dialogue. This vision may fail to correctly render the planning problem inherent to dialogue as well as its contextual and grounded nature. In this paper, we introduce a Deep Reinforcement Learning method to optimize visually grounded task-oriented dialogues, based on the policy gradient algorithm. This approach is tested on the question generation task from the dataset GuessWhat?! containing 120k dialogues and provides encouraging results at solving both the problem of generating natural dialogues and the task of discovering a specific object in a complex picture.

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A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version

Dialogue & Discourse ◽

10.5087/dad.2018.101 ◽

2018 ◽

Vol 9 (1) ◽

pp. 1-49 ◽

Cited By ~ 8

Author(s):

Iulian Vlad Serban ◽

Ryan Lowe ◽

Peter Henderson ◽

Laurent Charlin ◽

Joelle Pineau

Keyword(s):

Transfer Learning ◽

Expert Knowledge ◽

Learning Objective ◽

Data Driven ◽

Evaluation Metrics ◽

Dialogue Systems ◽

Speech And Language ◽

Language Understanding ◽

The Past ◽

Use Of Data

During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheless, several recent results suggest that data-driven approaches are feasible and quite promising. To facilitate research in this area, we have carried out a wide survey of publicly available datasets suitable for data-driven learning of dialogue systems. We discuss important characteristics of these datasets, how they can be used to learn diverse dialogue strategies, and their other potential uses. We also examine methods for transfer learning between datasets and the use of external knowledge. Finally, we discuss appropriate choice of evaluation metrics for the learning objective.

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Context- and Data-driven Satisfaction Analysis of User Interface Adaptations Based on Instant User Feedback

Proceedings of the ACM on Human-Computer Interaction ◽

10.1145/3331161 ◽

2019 ◽

Vol 3 (EICS) ◽

pp. 1-20 ◽

Cited By ~ 1

Author(s):

Enes Yigitbas ◽

André Hottung ◽

Sebastian Mansfield Rojas ◽

Anthony Anjorin ◽

Stefan Sauer ◽

...

Keyword(s):

User Interface ◽

User Feedback ◽

Data Driven ◽

Satisfaction Analysis

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Intel® Component Diagnostic Technology: Tools and Education for Intel Component Defect Reduction

ISTFA 2004: Conference Proceedings from the 30th International Symposium for Testing and Failure Analysis ◽

10.31399/asm.cp.istfa2004p0139 ◽

2004 ◽

Author(s):

Julie S. Doll

Keyword(s):

User Interface ◽

Data Management ◽

Contract Manufacturing ◽

Defect Reduction ◽

Web Based ◽

Technology Tools ◽

Diagnostic Capability ◽

Diagnostic Technology

Abstract To enable efficient, accurate debug of Intel architecture components to take place within contract manufacturing sites, and to provide alternatives for the removal of Intel components from, Intel is deploying a diagnostic capability and attendant educational collateral known as to achieve these objectives Intel® Component Diagnostic Technology. This paper will describe details of Intel® Component Diagnostic Technology, including the diagnostic fixture and user interface, diagnostic scripts and analytical coverage, data management and reporting, and on-site and Web-based educational offerings.

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