Transcript Learning and Inference

Global Inference and Learning
Towards Natural Language Understanding
Dan Roth
Department of Computer Science
University of Illinois at Urbana-Champaign
AAAI-06 Invited Talk
Page 1
Nice to Meet You
Page 2
Learning and Inference
 Global decisions in which several local decisions play a role
but there are mutual dependencies on their outcome.
 (Learned) classifiers for different sub-problems
 Incorporate classifiers’ information, along with constraints, in
making coherent decisions – decisions that respect the local
classifiers as well as domain & context specific constraints.
 Global inference for the best assignment to all variables of
interest.
Page 3
Comprehension
A process that maintains and
updates a collection of propositions
about the state of affairs.
(ENGLAND, June, 1989) - Christopher Robin is alive and well. He lives in
England. He is the same person that you read about in the book, Winnie the
Pooh. As a boy, Chris lived in a pretty home called Cotchfield Farm. When
Chris was three years old, his father wrote a poem about him. The poem was
printed in a magazine for others to read. Mr. Robin then wrote a book. He
made up a fairy tale land where Chris lived. His friends were animals. There
was a bear called Winnie the Pooh. There was also an owl and a young pig,
called a piglet. All the animals were stuffed toys that Chris owned. Mr. Robin
made them come to life with his words. The places in the story were all near
Cotchfield Farm. Winnie the Pooh was written in 1925. Children still love to
read about Christopher Robin and his animal friends. Most people don't know
he is a real person who is grown now. He has written two books of his own.
They tell what it is like to be famous.
1. Christopher Robin was born in England.
3. Christopher Robin’s dad was a magician.
2. Winnie the Pooh is a title of a book.
4. Christopher Robin must be at least 65 now.
Page 4
How to Address Comprehension?
(cartoon)
It’s an Inference Problem
Huge number of problems:
Map into a well defined language
Variability of Language
Use standard reasoning tools
Knowledge Acquisition
Reasoning Patterns
multiple levels of ambiguity
make language precise
Statistics: know a word by
its neighbors
Counting
Canonical Mapping? Underspecificity calls
for “purposeful”, goal specific mapping
Machine Learning
Classification Probabilistic models
Clustering
Structured Models
Page 5
What we Know: Stand Alone Ambiguity Resolution
Illinois’ bored of education
board
...Nissan Car and truck plant is …
…divide life into plant and animal kingdom
(This Art) (can N) (will MD) (rust V)
V,N,N
The dog bit the kid. He was taken to a veterinarian
a hospital
Learn a function f: X Y that
maps observations in a domain
to one of several categories or <
Broad Coverage
Page 6
Comprehension
A process that maintains and
updates a collection of propositions
about the state of affairs.
(ENGLAND, June, 1989) - Christopher Robin is alive and well. He lives in
England. He is the same person that you read about in the book, Winnie the
Pooh. As a boy, Chris lived in a pretty home called Cotchfield Farm. When
Chris was three years old, his father wrote a poem about him. The poem was
printed in a magazine for others to read. Mr. Robin then wrote a book. He
made up a fairy tale land where Chris lived. His friends were animals. There
was a bear called Winnie the Pooh. There was also an owl and a young pig,
called a piglet. All the animals were stuffed toys that Chris owned. Mr. Robin
made them come to life with his words. The places in the story were all near
Cotchfield Farm. Winnie the Pooh was written in 1925. Children still love to
read about Christopher Robin and his animal friends. Most people don't know
he is a real person who is grown now. He has written two books of his own.
They tell what it is like to be famous.
1. Christopher Robin was born in England.
3. Christopher Robin’s dad was a magician.
2. Winnie the Pooh is a title of a book.
4. Christopher Robin must be at least 65 now.
Page 7
This Talk
 Integrating Learning and Inference
 Historical Perspective
 Role of Learning
 Global Inference over Classifiers
 Semantic Parsing
 Multiple levels of processing
 Textual Entailment
 Summary and Future Directions
Page 8
Learning, Knowledge Representation, Reasoning


There has been a lot of work on these three topics in AI
But, mostly,



work on Learning, and
work on Knowledge Representation and Reasoning
Very little has been done on an integrative framework.





Inductive Logic Programming
Some work from the perspective of probabilistic AI
Some work from the perspective of Machine Learning (EBL)
Recent: Valiant’s Robust Logic
Learning to Reason
Page 9
Learning to Reason [’94-’97]: Key Insights

An unified framework to study Learning, Knowledge Representation and Reasoning

The goal is to Reason (deduction; abduction - best explanation)

Reasoning is not done from a static Knowledge Base but rather done with
knowledge that is learned via interaction with the world.

Intermediate Representation is important – but only to the extent that it is
learnable, and it facilitates reasoning.

Feedback to learning is given by the reasoning stage.

There may not be a need (or even a possibility) to learn the intermediate
representation exactly, but only to the extent that is supports Reasoning.
[Khardon & Roth JACM97, AAAI94; Roth95, Roth96, Khardon&Roth99
Learning to Plan: Khardon’99]
Page 10
Learning to Reason
Learning
W

Reasoning
Knowledge
Representation
KB
Task
Interaction with the World
Knowledge representation is:

- Chosen to facilitate inference

- Learned by interaction with the world


Performance is measured with respect to the world, on a reasoning task
Page 11
L2R: Computational Advantages
Deduction (Khardon,Roth 94,97)
f
q,
fCNF, q MonCNF
Page 12
L2R: Computational Advantages
Deduction (Khardon,Roth 94,97)
f
q,
fCNF, q MonCNF
• Learning to Reason is easy when Reasoning is hard
f
q
Page 13
L2R: Computational Advantages
Deduction (Khardon,Roth 94,97)
f
q,
fCNF, q MonCNF
• Learning to Reason is easy when Reasoning is hard
W
KB(f)
q
Page 14
L2R: Computational Advantages
Deduction (Khardon,Roth 94,97)
f
q,
fCNF, q MonCNF
• Learning to Reason is easy when Reasoning is hard
L
R
W
KB(f)
q
• Learning to Reason is easy when Learning is hard
L
W
f
Page 15
L2R: Computational Advantages
Deduction (Khardon,Roth 94,97)
f
q,
fCNF, q MonCNF
• Learning to Reason is easy when Reasoning is hard
L
R
W
KB(f)
q
•Learning to Reason is easy when Learning is hard
L
R
W
KB(f)
q
Page 16
Other studies on non-monotonic
reasoning as learning, etc.
L2R: Computational Advantages
Deduction (Khardon,Roth 94,97)
Learn a different
f
q,
fCNF,
q MonCNF
representation
for f,
oneisthat
allows
• Learning to Reason is easy when Reasoning
hard
L
R efficient reasoning
W
Learn a different
function, which only
•Learning
tof, Reason
is
approximates
but is
sufficient for exact
L
reasoning.
W
KB(f)
q
easy when Learning is hard
R
KB(f)
q
• No Magic
Page 17
Learning to Reason [’94-’97]:
Relevant?

An unified framework to study Learning, Knowledge Representation and Reasoning

The goal is to Reason (deduction; abduction - best explanation)

Reasoning is not done from a static Knowledge Base but rather done with
knowledge that is learned via interaction with the world.

Intermediate Representation is important – but only to the extent that it is
How?
learnable, and it facilitates reasoning.

Feedback to learning is given by the reasoning stage.

There may not be a need (or even a possibility) to learn the intermediate
representation exactly, but only to the extent that is supports Reasoning.
[Khardon & Roth JACM97, AAAI94; Roth95, Roth96, Khardon&Roth99
Learning to Plan: Khardon’99]
Page 18
Comprehension
A process that maintains and
updates a collection of propositions
about the state of affairs.
(ENGLAND, June, 1989) - Christopher Robin is alive and well. He lives in
England. He is the same person that you read about in the book, Winnie the
Pooh. As a boy, Chris lived in a pretty home called Cotchfield Farm. When
Chris was three years old, his father wrote a poem about him. The poem was
printed in a magazine for others to read. Mr. Robin then wrote a book. He
made up a fairy tale land where Chris lived. His friends were animals. There
was a bear called Winnie the Pooh. There was also an owl and a young pig,
called a piglet. All the animals were stuffed toys that Chris owned. Mr. Robin
made them come to life with his words. The places in the story were all near
Cotchfield Farm. Winnie the Pooh was written in 1925. Children still love to
read about Christopher Robin and his animal friends. Most people don't know
he is a real person who is grown now. He has written two books of his own.
They tell what it is like to be famous.
1. Christopher Robin was born in England.
3. Christopher Robin’s dad was a magician.
2. Winnie the Pooh is a title of a book.
4. Christopher Robin must be at least 65 now.
Page 19
Today’s Research Issues
It’s an Inference Problem

Learning serves to support abstraction




A context sensitive operation that is done at multiple levels
From names (Mr. Robin, Christopher Robin) to Relations (wrote,
author) and concepts
Learning serves to generate the vocabulary


What is the role of Learning?
over which reasoning is possible (Part-of-speech; a subject-of,…)
Knowledge Acquisition; tuning; memory,….
Learning in the context of a reasoning system



Training with an Inference Mechanism
Feedback is done at the inference level, not at the single classifier level
Labeling using Inference
Page 20
This Talk
 Integrating Learning and Inference
 Historical Perspective
 Role of Learning
 Global Inference over Classifiers
 Semantic Parsing
 Multiple levels of processing
 Textual Entailment
 Summary and Future Directions
Page 21
What I will not talk about: Knowledge Representation

A unified representation that is used


as an input to learning processes, and
as an output of learning processes
Mohammed Atta met with an Iraqi
intelligence agent in Prague in April 2001.
mee
ting
location
time
participa
nt
pers
on
nationalit
y
affiliatio
n
……
cou
ci
Specifically, we use:
ntry
ty
 An abstract representation that is centered around a semantic
parse (predicate-argument representation) augmented by
additional information.
 Formalized as a hierarchical concept graph (Description Logic
inspired)

name(Pragu
e)

organizatio
date
n
month(April)name(Iraq
)
year(2001)
Feature Description Logic [Cumby&Roth’00, 02, 03]
Page 22
Inference and Learning
 Global decisions in which several local decisions play a role but
there are mutual dependencies on their outcome.
 Learned classifiers for different sub-problems
 Incorporate classifiers’ information, along with constraints, in
making coherent decisions – decisions that respect the local
classifiers as well as domain & context specific constraints.

How to induce a predicate argument representation of a sentence.
How to use
inferencefor
methods
over learned
outcomes.
Global
inference
the best
assignment
to all variables of
How to use declarative information over/along with learned information.
interest.
Page 23
Semantic Role Labeling
I left my pearls to my daughter in my will .
[I]A0 left [my pearls]A1 [to my daughter]A2 [in my will]AM-LOC .




Special Case (structure output problem):
A0
Leaver
here, all the data is available at one time;
in general, classifiers might be learned
A1
Things left
from different sources, at different times,
A2
Benefactor
at different contexts.
AM-LOC
Location
Implications on training paradigms
I left my pearls to my daughter in my will .
Overlapping arguments
If A2 is present, A1
must also be present.
Page 24
Problem Setting

Random
y1
Variables Y:
y4
C(y1,y4)
y5
y2
y6
C(y2,y3,y6,y7,y8)
y7
y3
y8

Conditional Distributions P (learned by classifiers)
Constraints C– any Boolean function
defined on partial assignments (possibly: + weights W )

Goal: Find the “best” assignment

The assignment that achieves the highest global accuracy.
This is an Integer Programming Problem


Y*=argmaxY PY (+ WC) subject to constraints C
Page 25
Semantic Role Labeling (1/2)
Who did what to whom, when, where, why,…
For each verb in a sentence

1.
2.
Identify all constituents that fill a semantic role
Determine their roles
•
•
Core Arguments, e.g., Agent, Patient or Instrument
Their adjuncts, e.g., Locative, Temporal or Manner
A0
:utterance
leaverleft
A1::thing
A1
A2
: benefactor
C-A1
: utterance
A0 : leaver
I pearls
left
mywhich
pearls
towere
my
in my
will.
The
The
pearls,
I said,
I left
to daughter-in-law
left
my to
daughter-in-law
my daughter-in-law.
are
fake.
A1 : thing
R-A1
A0
: left
sayer
A2 : benefactor
AM-LOC
Page 26
Semantic Role Labeling (2/2)

PropBank [Palmer et. al. 05] provides a large human-annotated
corpus of semantic verb-argument relations.



Core arguments: A0-A5 and AA



It adds a layer of generic semantic labels to Penn Tree Bank II.
(Almost) all the labels are on the constituents of the parse trees.
different semantics for each verb
specified in the PropBank Frame files
13 types of adjuncts labeled as AM-arg

where arg specifies the adjunct type
Page 27
Identify Vocabulary
Algorithmic Approach


I left my nice pearls to her
[ [
[
[
[
]
] ]
]
]
Pruning [Xue&Palmer, EMNLP’04]
Argument Identifier

Binary classification (SNoW)
Classify argument candidates

Argument Classifier


candidate arguments
Identify argument candidates


I left my nice pearls to her
Multi-class classification (SNoW)
Inference



Inference over (old and
I left my nice pearls to her
[new)
[
[ Vocabulary
[
[
]I left
] my
] nice pearls] to her
]
Use the estimated probability distribution
given by the argument classifier
Use structural and linguistic constraints
Infer the optimal global output
I left my nice pearls to her
Page 28
Argument Identification & Classification
I left my nice pearls to her
[ [
[
[
[
]
] ]
]
]

Both argument identifier and argument
classifier are trained phrase-based classifiers.

Features (some examples)

voice, phrase type, head word, path, chunk,
chunk pattern, etc. [some make use of a full syntactic parse]
I left my nice pearls to her

Learning Algorithm – SNoW

Sparse network of linear functions


weights learned by regularized Winnow multiplicative update rule
Probability conversion is done via softmax pi = exp{acti}/j exp{actj}
Page 29
I left my nice pearls to her
Inference

The output of the argument classifier often violates some
constraints, especially when the sentence is long.

Finding the best legitimate output is formalized as an
optimization problem and solved via Integer Linear
Programming.
[Punyakanok et. al 04, Roth & Yih 04]

Input:



The probability estimation (by the argument classifier)
Structural and linguistic constraints
Allows incorporating expressive (non-sequential)
constraints on the variables (the arguments types).
Page 30
Integer Linear Programming (ILP)
Maximize:
Subject to
Page 31
Integer Linear Programming Inference

For each argument ai


Set up a Boolean variable: ai,t indicating whether ai is classified as t
Goal is to maximize


 i score(ai = t ) ai,t
Subject to the (linear) constraints


Any Boolean constraints can be encoded as linear constraint(s).
If score(ai = t ) = P(ai = t ), the objective is to find the
assignment that maximizes the expected number of
arguments that are correct and satisfies the constraints.
Page 32
Inference

Maximize expected number correct


 i P( ai = ti )
Subject to some constraints


T* = argmaxT
I left my nice pearls to her
Structural and Linguistic (R-A1A1)
Solved with Integer Learning Programming
0.3 0.2 0.2 0.3
0.6 0.0 0.0 0.4
0.1 0.3 0.5 0.1
I left my nice pearls to her
0.1 0.2 0.3 0.4
Cost = 0.3 + 0.4
0.6 + 0.5
0.3 + 0.4 = 1.8
1.6
1.4
Non-Overlapping
BlueRed
Independent
&Max
N-O
Page 33
Constraints

No duplicate argument classes
a  POTARG x{a = A0}  1

Any Boolean rule can be encoded as
a linear constraint.
R-ARG
If there is an R-ARG phrase, there is an ARG
Phrase
 a2  POTARG , a  POTARG x{a = A0}  x{a2 = R-A0}

C-ARG

a2  POTARG ,
If there is an C-ARG phrase, there is an ARG before it
 (a  POTARG)  (a is before a2 ) x{a = A0}  x{a2 = C-A0}
Many other possible constraints:




Unique labels
No overlapping or embedding
Relations between number of arguments
If verb is of type A, no argument of type B
Universally quantified
rules
Page 34
Semantic Parsing: Summary I


This approach produces a very good semantic parser.
Top ranked system in CoNLL’05 shared task:




Key difference is the Inference
Easy and fast: ~1 Sentence/Second
(using Xpress-MP)
A lot of room for improvement (additional constraints)
Demo available http://L2R.cs.uiuc.edu/~cogcomp
 Significant


also in enabling knowledge acquisition
So far, shown the use of only declarative (deterministic) constraints.
In fact, this approach can be used both with statistical and declarative
constraints.
Page 35
ILP as a Unified Algorithmic Scheme

Consider a common model for sequential inference: HMM/CRF

Inference in this model is done via
the Viterbi Algorithm.
s

A
B
C
A
B
C
A
B
C
A
B
C
A
B
C
t
y y1 y2 y3 y4 y5
x x1 x2 x3 x4 x5
Viterbi is a special case of the Linear Programming based Inference.

Viterbi is a shortest path problem, which is a LP, with a canonical matrix that is
totally unimodular. Therefore, you can get integrality constraints for free.
One can now incorporate non-sequential/expressive/declarative constraints by
modifying this canonical matrix
 The extension reduces to a polynomial scheme under some conditions (e.g.,
when constraints are sequential, when the solution space does not change, etc.)
 Not necessarily increases complexity and very efficient in practice
[Roth&Yih, ICML’05]

Page 36
Integer Linear Programming Inference - Summary

An Inference method for the “best explanation”, used here
to induce a semantic representation of a sentence.


Allows expressive constraints


Any Boolean rule can be represented by a set of linear (in)equalities
Combining acquired (statistical) constraints with declarative
constraints



A general Information Integration framework.
Start with shortest path matrix and constraints
Add new constraints to the basic integer linear program.
Solved using off-the-shelf packages
If the additional constraints don’t change the solution, LP is enough
 Otherwise, the computational time depends on sparsity; fast in practice


Demo available http://L2R.cs.uiuc.edu/~cogcomp
Page 37
This Talk
 Integrating Learning and Inference
 Historical Perspective
 Role of Learning
 Global Inference over Classifiers
 Semantic Parsing
 Multiple levels of processing
 Textual Entailment
 Summary and Future Directions
Page 38
Inference and Learning

Global decisions in which several local
decisions play a role but there are
mutual dependencies on their
outcome.

So far, this was a single stage process.





Learn (acquire a new vocabulary) and
Run inference over it to guarantee the
coherency of the outcome.
Is that it?
Of course, this isn’t sufficient.
The process of learning and Inference
needs to be done in phases.
It’s turtles all the way down…
Page 39
Pipeline
Raw Data
POS Tagging






 Vocabulary is generated in phases
 Left to Right processing of sentences is also a pipeline process
Phrases
Semantic Entities
Parsing
WSD
Relations
Semantic Role Labeling
Pipelining is a crude approximation; interactions occur across levels and
down stream decisions often interact with previous decisions.
Leads to propagation of errors
Occasionally, later stage problems are easier but upstream mistakes will not
be corrected.
There are good reasons for pipelining decisions
Global inference over the outcomes of different levels can be used to break
away from this paradigm. [between pipeline & fully global]
Allows a flexible way to incorporate linguistic and structural constraints.
Page 40
Entities and Relations: Information Integration
J.V. Oswald was murdered at JFK after his assassin, K. F. Johns…
Identify:
Kill (X, Y)
person



person
J.V. Oswald was murdered at JFK after his assassin, K. F. Johns…
location
Some knowledge (classifiers)
may be known in advance
Some constraints may be
Identify named entities
available only at decision time
Identify relations between entities
Exploit mutual dependencies between named entities and relation to
yield a coherent global detection.
[Roth & Yih, COLING’02;CoNLL’04]
Page 41
This Talk
 Integrating Learning and Inference
 Historical Perspective
 Role of Learning
 Global Inference over Classifiers
 Semantic Parsing
 Multiple levels of processing
 Textual Entailment
 Summary and Future Directions
Page 42
Comprehension
A process that maintains and
updates a collection of propositions
about the state of affairs.
(ENGLAND, June, 1989) - Christopher Robin is alive and well. He lives in
England. He is the same person that you read about in the book, Winnie the
Pooh. As a boy, Chris lived in a pretty home called Cotchfield Farm. When
Chris was three years old, his father wrote a poem about him. The poem was
printed in a magazine for others to read. Mr. Robin then wrote a book. He
made up a fairy tale land where Chris lived. His friends were animals. There
was a bear called Winnie the Pooh. There was also an owl and a young pig,
called a piglet. All the animals were stuffed toys that Chris owned. Mr. Robin
made them come to life with his words. The places in the story were all near
Cotchfield Farm. Winnie the Pooh was written in 1925. Children still love to
read about Christopher Robin and his animal friends. Most people don't know
he is a real person who is grown now. He has written two books of his own.
They tell what it is like to be famous.
1. Christopher Robin was born in England.
3. Christopher Robin’s dad was a magician.
2. Winnie the Pooh is a title of a book.
4. Christopher Robin must be at least 65 now.
Page 43
By “semantically entailed” we mean:
Phrasal
verbagree
paraphrasing
most people
would
that one
Textual Entailment
[Connor&Roth’06]
sentence
implies the other.
Simply – making plausible inferences
 Given:
Entity matching [Li et. al,
Q: Who acquired Overture?
AAAI’04, NAACL’04]
 Determine:
A: Eyeing the huge market potential, currently
Semantic Role Labeling
led by Google, Yahoo took over search company
Overture Services Inc last year.
Eyeing the huge market
potential, currently led by
Google, Yahoo took over
search company
Overture Services Inc. last
year
Entails
Subsumed by

Yahoo acquired Overture
Overture is a search company
Google is a search company
Google owns Overture
……….
Page 44
Discussing Textual Entailment

Requires an inference process that makes use of a large number of
learned (and knowledge-based) operators.
A sound approach for determining whether a statement of interest holds in
a given sentence. [Braz et. al, AAAI05]
 A pair (Sentence, hypothesis) is transformed into a simpler pair, in an
entailment preserving manner.
 Constrained Optimization formulation, over a large number of learned
operators. Aimed at the best (simplest) mapping between predicateargument representations.


Inference is purposeful: No canonical representation, but rather
reasoning on sentences transformation depends on the hypothesis.

What is shown next is a proof.
At any stage, a large number of operators are entertained, some do not
fire, some lead nowhere.
This is a path through the optimization process, that leads to a justifiable
(and explainable) answer.


Page 45
Sample Entailment Pair
S: Hurricane Katrina petroleum-supply outlook improved
somewhat, yesterday, as U.S. and European governments
finally reached a consensus.
They finally made up their minds to release 2 million barrels
a day, of oil and refined products, from their reserves.
T: Offers by individual European governments involved
supplies of crude or refined oil products.
Does ‘T’ follow from ‘S’?
Page 46
OPERATOR 1: Phrasal Verb
Replace phrasal verbs
with an equivalent single word verb
S: Hurricane Katrina petroleum-supply outlook improved
somewhat, yesterday, as U.S. and European governments
finally reached a consensus.
They finally made up their minds to release 2 million
barrels a day, of oil and refined products, from their
reserves.
T: Offers by individual European governments involved
supplies of crude or refined oil products.
S: Hurricane Katrina petroleum-supply outlook improved
somewhat, yesterday, as U.S. and European governments
finally reached a consensus.
They finally decided to release 2 million barrels a day, of
oil and refined products, from their reserves.
T: Offers by individual European governments involved
supplies of crude or refined oil products.
Page 47
S: Hurricane Katrina petroleum-supply outlook improved
somewhat, yesterday, as U.S. and European governments
finally reached a consensus.
U.S. and European governments finally decided to release
2 million barrels a day, of oil and refined products, from
their reserves.
T: Offers by individual European governments involved
supplies of crude or refined oil products.
OPERATOR 2: Coreference Resolution
Replace pronouns/possessive pronouns
with the entity to which they refer
S: Hurricane Katrina petroleum-supply outlook improved
somewhat, yesterday, as U.S. and European governments
finally reached a consensus.
They finally decided to release 2 million barrels a day, of
oil and refined products, from their reserves.
T: Offers by individual European governments involved
supplies of crude or refined oil products.
Page 48
S: Hurricane Katrina petroleum-supply outlook improved
somewhat, yesterday, as U.S. and European governments
finally reached a consensus.
U.S. and European governments finally decided to release
2 million barrels a day, of oil and refined products, from
their reserves.
T: Offers by individual European governments
governments,involved
involved
supplies of crude or refined oil products.
OPERATOR 3: Focus of Attention
Remove segments of a sentence that do not appear to be necessary;
may allow more accurate annotation of remaining words
S: U.S. and European governments finally decided to release
2 million barrels a day, of oil and refined products, from
their reserves.
T: Offers by individual European governments involved
supplies of crude or refined oil products.
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OPERATOR 4: Nominalization Promotion
Replace a verb that does not express a useful/meaningful relationship
with a nominalization in one of its arguments
involved
supplies …
Offers
S: U.S. and European governments finally
decided to release 2 million barrels a
day, of oil and refined products, from
their reserves.
T: Individual European governments
offered supplies of crude or refined oil
products.
by individual …
offered
Individual …
Requires semantic
role labeling (for
noun predicates)
supplies …
S: U.S. and European governments finally decided to release
2 million barrels a day, of oil and refined products, from
their reserves.
T: Offers by individual European governments involved
supplies of crude or refined oil products.
Page 50
OPERATOR 4: Nominalization Promotion
Replace a verb that does not express a useful/meaningful relationship
with a nominalization in one of its arguments
offered
Individual …
supplies
of crude …
S: U.S. and European governments finally
decided to release 2 million barrels a
day, of oil and refined products, from
their reserves.
T: Individual European governments
offered supplies of crude or refined oil
products.
supplied
Individual …
crude …
S: U.S. and European governments finally
decided to release 2 million barrels a
day, of oil and refined products, from
their reserves.
T: Individual European governments
supplied crude or refined oil products.
Page 51
OPERATOR 5: Predicate Embedding Resolution
Replace a verb compound where the first verb may indicate modality or negation
with a single verb, marked with negation/modality attribute
‘decided’ (almost) does not change the
But what if the embedding verb had
meaning of the embedded verb
been ‘refused’?
ENTAILMENT SHOULD NOT BE SUCCEEDED
decided
refused
U.S. and …
release
2 million barrels
a day, of oil …
negation
released
U.S. and …
2 million barrels
a day, of oil …
Requires semantic
role labeling (for
noun predicates)
S: U.S. and European governments finally
released 2 million barrels a day, of oil
and refined products, from their
reserves.
T: Individual European governments
supplied crude or refined oil products.
S: U.S. and European governments finally
decided to release 2 million barrels a
day, of oil and refined products, from
their reserves.
T: Individual European governments
supplied crude or refined oil products.
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OPERATOR 6: Predicate Matching
System matches PREDICATES and their ARGUMENTS
-- accounts for monotonicity, modality, negation, and quantifiers
ENTAILMENT SUCCEEDS
Requires lexical
abstraction
S: U.S. and European governments finally
released 2 million barrels a day, of oil
and refined products, from their
reserves.
T: Individual European governments
supplied crude or refined oil products.
S: U.S. and European governments finally
released 2 million barrels a day, of oil
and refined products, from their
reserves.
T: Individual European governments
supplied crude or refined oil products.
Page 53
Conclusions
Discussed a general paradigm for learning and inference in the
context of natural language understanding tasks
Did not discuss:



Knowledge Representation
How to train?



Key insight – what to learn is driven by global decisions.
Luckily: # of components is much smaller than # of decisions.
Emphasis should be on

Learn Locally and make use globally (via global inference)


[Punyakanok et. al IJCAI’05]
Ability to make use of domain & constraints to drive supervision

[Klementiev & Roth, ACL’06]
Page 54
Conclusions (2)

Discussed a general paradigm for learning and inference in the
context of natural language understanding tasks
 Incorporate classifiers’ information, along with expressive constraints,
within an inference framework for the best explanation.

We can now incorporate many good old ideas.

Learning allows us to develop the right vocabulary, and
supports appropriate abstractions so that we can study natural
language understanding as a problem of reasoning.

Room for new research on reasoning patterns in NLP
Page 55
Acknowledgement

Many of my students contributed significantly to this line
of work
Vasin Punyakanok, Scott Yih, Mark Sammons
Xin Li, Dav Zimak, Rodrigo de salvo Braz,
Chad Cumby, Yair Even Zohar, Michael Connor;
Kevin Small, Alex Klementiev

Funding




ARDA, under the AQUAINT program
NSF: ITR IIS-0085836, ITR IIS-0428472; ITR IIS- 0085980
A DOI grant under the Reflex program,
DASH Optimization
Page 56
Questions?

Thank you
Page 57