From Question-Answering to Information-Seeking Dialogs Jerry R. Hobbs Artificial Intelligence Center...

From Question-Answeringto Information-Seeking Dialogs

Jerry R. Hobbs

Artificial Intelligence Center

SRI International

Menlo Park, California

(with Douglas Appelt, Chris Culy, David Israel, David Martin, Martin Reddy, Mark Stickel, and Richard Waldinger)

05/15/02Principal Investigator: Jerry R. Hobbs, SRI International 2

Key Ideas

1. Logical analysis/decomposition of questions into component questions, using a reasoning engine

2. Bottoming out in variety of web resources and information extraction engine

3. Use of component questions to drive subsequent dialogue, for elaboration, revision, and clarification

4. Use of analysis of questions to determine, formulate, and present answers.


Plan of Attack

Inference-Based System:

Inference for Question-Answering -- this year Inference for Dialog Structure -- next year, but starting design this year

Document retrieval and information extraction for question-answering:

Incorporate as resource in inference-based system -- this year


Composition of Informationfrom Multiple Sources

How far is it from Mascat to Kandahar?

What is the lat/longof Mascat?

What is the distancebetween the two lat/longs?

What is the lat/longof Kandahar?

AlexandrianDigital Library

Gazetteer Geographical Formula

orwww.nau.edu/~cvm/latlongdist.html

QuestionDecomposition

via Logical Rules

ResourcesAttached toReasoning

Process


Gazetteer

GEMINI

SNARK


Composition of Informationfrom Multiple Sources

Show me the region 100 km north of the capital of Afghanistan.

What is the capitalof Afghanistan?

What is the lat/long100 km north?

What is the lat/longof Kabul?

CIAFact Book Geographical

Formula


via Logical Rules


Gazetteer

Show thatlat/long

Terravision


Process


Combining Time, Space,and Personal Information

Could Mohammed Atta have met with an Iraqi official between 1998 and 2001?

IE Engine

GeographicalReasoning


via Logical Rules

ResourceAttached toReasoning

Process

meet(a,b,t) & 1998 t 2001

at(a,x1,t) & at(b,x2,t) & near(x1,x2) & official(b,Iraq)

go(a,x1,t) go(b,x2,t)

IE Engine

TemporalReasoning

Logical Form


Two Central Systems

GEMINI: Large unification grammar of English Under development for more than a decade Fast parser Generates logical forms Used in ATIS and CommandTalk

SNARK: Large, efficient theorem prover Under development for more than a decade Built-in temporal and spatial reasoners Procedural attachment, incl for web resources Extracts answers from proofs Strategic controls for speed-up


Linguistic Variation

How far is Mascat from Kandahar?How far is it from Mascat to Kandahar?How far is it from Kandahar to Mascat?How far is it betweeen Mascat and Kandahar?What is the distance from Mascat to Kandahar?What is the distance between Mascat and Kandahar?

GEMINI parses and produces logical forms for most TREC-type queriesUse TACITUS and FASTUS lexicons to augment GEMINI lexiconUnknown word guessing based on "morphology" and immediate context


"Snarkification"

Problem: GEMINI produces logical forms not completely aligned with what SNARK theories need

Current solution: Write simplification code to map from one to the other

Long-term solution: Logical forms that are aligned better


Relating Lexical Predicates

to Core Theory Predicates

"... distance ..." "how far ..."

distance-between

Need to write these axioms for every domain we deal withHave illustrative examples


Decomposition of Questions

lat-long(l1,x) & lat-long(l2,y) & lat-long-distance(d,l1,l2) --> distance-between(d,x,y)

Need axioms relating core theory predicates and predicates from available resourcesHave illustrative examples


Procedural Attachment

Declaration for certain predicates: There is a procedure for proving it Which arguments are required before called lat-long(l1,x) lat-long-distance(d,l1,l2)

When predicate with those arguments bound is generated in proof, procedure is exectuted.


Open Agent Architecture

OAA Agent

GEMINI snarkify SNARK

Resources viaOAA Agents


Use of SMART + TextProQuestion

Subquestion-1

Other Resources


via Logical Rules


Process

Subquestion-2

Subquestion-3

SMART + TextPro

OneResource

AmongMany


Information ExtractionEngine as a Resource

SMART: Document retrieval for pre-processing

TextPro: Top of the line information extraction engine

Analyze NL query w GEMINI and SNARK

Run TextPro over documents retrieved by SMART

Retrieve best-match passage

Use TextPro annotations or GEMINI analysis to extract answer from passage


Linking SNARK with TextPro

TextSearch(EntType(?x), Terms(p), Terms(c), WSeq)

& Analyze(WSeq, p(?x,c))

--> p(?x,c)

Call to SMART+TextPro

Type of questionedconstituent

Synonyms and hypernymsof word associated with p or c

Answer:Ordered sequenceof strings of words

Match pieces of answer stringswith pieces of querySubquery generated by SNARK

during analysis of query


Information ExtractionEngine as a Resource

SMART: Document retrieval for pre-processing

TextPro: Top of the line information extraction engine

Analyze NL query w GEMINI and SNARK

Run TextPro over documents retrieved by SMART

TextPro returns relevant templates

Agent turns templates into logic for SNARK to use in proof


Domain-Specific Patterns

Decide upon domain (e.g., nonproliferation)

Compile list of principal properties and relations of interest

Implement these patterns in TextPro

Implement link between TextPro and SNARK, converting between templates and logic


Temporal Reasoning: Structure

Topology of Time: start, end, before, between

Measures of Duration: for an hour, ...

Clock and Calendar: 3:45pm, Wednesday, June 12

Temporal Aggregates: every other Wednesday

Deictic Time: last year, ...


Temporal Reasoning: Goals

Develop temporal ontology (DAML)

Reason about time in SNARK (AQUAINT, DAML)

Link with Temporal Annotation Standards (AQUAINT)

Answer questions with temporal component (AQUAINT)

Nearly complete

In progress


Spatial and GeographicalReasoning: Structure

Topology of Space: Is Albania a part of Europe?

Dimensionality

Measures: How large is North Korea? Orientation and Shape: What direction is Monterey from SF?

Latitude and Longitude: Alexandrian Digital Library Gazetteer

Political Divisions: CIA World Fact Book, ...


Spatial and GeographicalReasoning: Goals

Develop spatial and geographical ontology (DAML)

Reason about space and geography in SNARK (AQUAINT, DAML)

Attach spatial and geographical resources (AQUAINT)

Answer questions with spatial component (AQUAINT)

Somecapability

now


Dialog Modeling

Key Idea: System matches user's utterance with one of several active tasks. Understanding dialog is one active task.

Rules of form:

property(situation) --> active(Task1)

including

utter(u,w) --> active(DialogTask) want(u,Task1) --> active(Task1)

Understanding is matching utterance (conjunction of predications) with an active task or the condition of an inactive task.


Dialog Task Model

understand(a,e,t): hear(a,w) & parse(w,e) & match(e,t)

yes Action determinedby utterance and

task

no -- x unmatched

Ask about x


Fixed-Domain QA Evaluation

Pick a domain, e.g., nonproliferation

Pick a set of resources, including a corpus of texts, structured databases, web services

Have expert make up 200+ realistic questions, answerable with resources + inference

Divide questions into training and test sets

Give sites one month+ to work on training set

Test on test set

From Question-Answering to Information-Seeking Dialogs Jerry R. Hobbs Artificial Intelligence Center...

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