Transcript Dialogue and Information Retrieval

Dialogue and Information Retrieval
Dialogs on Dialogs
March all the way through April 2003
Intersections between
Dialog Systems and IR
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Current work
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Call Routing
Question Answering
Why so little?
What else? Let’s brainstorm!
Call Routing
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Task: given a NL expression of a problem,
classify (route) it in one of several
categories
Examples
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AT&T: How May I Help You
British Telecom
Jennifer Chu-Carroll
Call Routing (2)
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It’s a classification problem!
Salience (co-ocurrence) based
approaches (AT&T)
IR-like approaches (J. Chu-Carroll)
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Treat user requests as “documents”
Use VSM and cosine similarity to classify
The IR in Call Routing
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Regard the problem as text classification
Do standard IR work:
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LSA
LDA
Centroid vs. KNN approaches
Results? Classification perf?
The Dialog in Call Routing
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Disambiguation
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Follow-up dialog
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Easy to do based on the VSM IR approach
HMIHY: frame-based follow-up dialogs
Q: Is Call Routing dialog management?
Q: Or is it more like understanding?
Q: Why typical understanding/DM
approaches fail in HMIHY-type domains?
Question Answering
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Task: answer to a question in Natural
Language from a database of documents
in Natural Language.
Examples:
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http://www.ai.mit.edu/projects/infolab/
http://www.ask.com
IR in Question Answering
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Everywhere:
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Document indexing
Retrieval
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What is different from traditional IR?
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Some parsing/understanding of questions and
documents
Some language generation (?)
Dialog in QA
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Refining the question:
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Clarification dialogue
Decide which question to ask
Only for very restricted domains  uses
fixed frames (Rutgers: HITIQA)
Why so little?
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Different issues:
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IR = lots of unstructured data, no NLP
Dialog = structured data, lots of NLP
Main problems:
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Structural mismatch
NLU mismatch
But HUGE potential!
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Voice-only random access to large
amounts of information (“Voice IR”):
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technical manuals of “in-the-field” devices
(e.g. NASA)
tutorial systems
phone-based Google (e.g. legal information…)
GUI+ for IR
Learn dialog stuff from data (LM, NLG,
parsing…)