The document discusses advancements in dialog systems and virtual assistants, highlighting the importance of combining symbolic methods with machine learning for improved performance. It outlines the evolution of AI as a conversational user interface and reviews the current landscape of natural language processing and understanding. Additionally, it emphasizes research on dialogue coherence, turn-taking, and knowledge representation to enhance artificial agents' conversational abilities.

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Logic and Probabilistic
Methods for Dialog
Valeria de Paiva & Charlie Ortiz
ESSLLI Barcelona, 10-14 August 2015

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Vision
Overview of work on dialog
to organize our own

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+
Ron Kaplan “Beyond the GUI: It’s Time for a
Conversational User Interface”, Wired 2013

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Source: http://www.bloomberg.com/news/articles/2015-02-03/i-ll-be-back-the-return-of-artificial-intelligence
Setting the Scene: AI as an ecosystem

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Functional Areas
Big players
Prediction Knowledge Retrieval Sentiment Personalization Learning Summarization VA
Hundreds of startups & many capabilities

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Personalized & proactive. Consistent assistance on every channel.
Intelligence is an expectation
Knows how to talk.

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Siri
Oct 2011
Google Now
Jul 2012
Nina
Aug 2012
USAA EVA
CVS
Cortana
Aug 2014
Dom
Oct 2014
USAA Coach
May 2015
Voice based Virtual Assistants paving
the way for general acceptance of AI
INGE
Lily
May 2013
Tangerine
Dragon
Oct 2012

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CoreASR performance
• Continuous server ASR word error rate
(WER) reduction ~18% / year:
combination of algorithms, data, and
computing
• Deep learning (DNNs) is driving recent
performance improvements in ASR and
meaning extraction

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DeepLanguageUnderstanding
Symbolic methods complement machine learning in a common architecture
Amount of domain-specific data
Accuracy
Combined
Symbolic-only
Data only

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Structured
Data
VA Building Tools & Runtime
(NLU + Dialog + Knowledge)
SDK/API
How Virtual Assistants are built today…

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NLP + Dialog + Knowledge?

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Dialogue, Dialog
– (Raquel Fernández, Oxford Bibliographies, 2013)
– Research on dialogue is concerned with the study of how
communication takes place through language in conversation.
– Investigates what makes a dialogue coherent;
– how dialogue participants coordinate to take turns in speaking and to
achieve mutual understanding;
– and how knowledge about how dialogue works can be used to
design artificial agents that are able to converse with humans.
– By now well-established field of research at the interface of
linguistics, psycholinguistics, sociolinguistics, and artificial
intelligence.
– Ginzburg, Jonathan, and Raquel Fernández. 2010. Computational models
of dialogue. In Handbook of computational linguistics and natural language
processing. Edited by Alexander Clark, Chris Fox, and Shalom Lappin

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Timetable

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Let the fun begin!

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Recap:

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So far:
synergy here: 3 courses, workshop, plus
Invited Talk
wide area, very different approaches,
lots left uncovered, e.g. ellipsis, (more)
discourse, formal models of game-
boards?
consensus on annotations/
segmentation?

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Taking home more than
Elephant theory of dialog

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Matrix of genres/emphasis

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Deepak:
I’m interested in building systems
that can handle simple dialogs but
do so in a robust manner.

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Deepak again…

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Deepak:
Growing POT for everyone…
(loads that I don’t understand)
1st Dialog State Tracking Challenge
Relational Belief Tracking
[Ramachandran & Ratnaparkhi’15]
Belief Tracking with Tree Surgery

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Further Plans?
Shall we do another
workshop?
Shall we do a survey
publication?

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Thank you!