1 knowledge representation and inference models for textual entailment dan roth university of...

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Knowledge Representation and Inference Models

for Textual Entailment

Dan Roth

University of IllinoisUrbana-Champaign

withRodrigo Braz, Roxana Girju, Vasin Punyakanok, Mark Sammons

By “textually entailed” we mean: most people would agree that

one sentence implies the other. (more later)

Fundamental Task

WalMart defended itself in court today against claims that its female employees were kept out of jobs in management because they are women

WalMart was sued for sexual discrimination

Entails

Subsumed by

A fundamental task that can be used as a building block in multiple NLP and information extraction applications

There is always a risk in solving a separate ’fundamental’ task rather than the task one really wants to solve…

Some of the examples here are very direct, though.

Has multiple direct applications

Why Textual Entailment?

Given: Q: Who acquired Overture? Determine: A: Eyeing the huge market potential,

currently led by Google, Yahoo took over search

company Overture Services Inc last year.

Question Answering

Eyeing the huge market potential, currently led by Google, Yahoo took over search company Overture Services Inc last year

Yahoo acquired Overture

Entails

Subsumed by

(and distinguish from other candidates)

A process that maintains and updates a collection of propositions about the state of affairs.

Viewed this way, a fundamental task to consider is that of textual entailment: Given a snippet of text S, does it entail a proposition T?

Story Comprehension

(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. He has written books of his own that tell what it is like to be famous. [REMEDIA]

1. Christopher Robin was born in England. 2. Winnie the Pooh is a title of a book.

3. Christopher Robin’s dad was a magician. 4. Christopher Robin must be at least 65 now.

A key problem in natural language understanding is to abstract over the inherent syntactic and semantic variability in natural language.

Multiple tasks attempt to do just that. Relation Extraction:

Dole’s wife, Elizabeth, is a native of Salisbury, N.C. Elizabeth Dole was born in Salisbury, N.C

Information Integration (Data Bases)Different database schemas represent the same information under different titles.

Information retrieval:Multiple issues, from variability in the query and target text, to relations

Summarization Multiple techniques can be applied; all are entailment

problems.

More ExamplesYou may disagree with the truth of this statement; and you may infer also that:

the presidential candidate’s wife was born in N.C.

Direct Application: Semantic Verification

(and distinguish from other candidates)

Given: A long contract that you need to

ACCEPT Determine:

Does it satisfy the 3 conditions that you really

care about?

ACCEPT?

A fundamental task for language comprehension.

Builds on a lot of research (and tools) done in the last few years in Learning and Inference in Natural Language.

Opens up a large collection of questions both from the natural language perspective and from the machine learning, knowledge representation and inference perspectives.

Why Study Textual Entailment?

A brief perspective & technical motivation

An Approach to Textual Entailment The CCG Inference model for textual entailment Inference as optimization

Some examples Knowledge modules

Conclusions

This Talk

Statistics: Using relatively simple statistical techniques for BOW and/or paraphrases Multiple problems that may not be addressed just from the

data: Entailment vs. Correlation [Geffet & Dagan’s 04,05]

An important component, but: How to put together/chain/weigh paraphrases? Inference

model. Inference in NL requires mapping sentences to

logical forms and using general purpose theorem proving. Extensions include various relaxations in the way the

representation is generated and in the type of information incorporated in a KB, to support the theorem prover; non-logical, probabilistic paradigms.

Key problems include the realization that underspecificty of the language is a feature, rather than a bug.

representation, but not a canonical representation

Two Extremes in Representation and Inference

Access to information requires tolerating “loose speak” [Porter et. al, ‘04]

Refers to the imprecise way queries/questions are formed – with respect to the representation of the information source. Metonymy: referring to the an entity or event by one of its

attributes Causal factor: referring to a result by one of its causes Aggregate: referring to an aggregate by one of its members Generic: referring to a specific concept by the generic class

to which it belongs [The potato was cultivated first in SA]

Noun compounds: referring to a relation between nouns by using just the noun phrase consisting of the two nouns. [wooden table]

Many other kinds of ambiguities – some language related and some knowledge related.

New (Better?) View on Problems

Collin Powel addressed the general assembly yesterday

Collin Powel gave a speech at the UN The secretary of state gave a speech at

the UN

Resolving the sense ambiguity in “addressed” ? Or a weaker, “existential”, Yes/No with respect to “gave a

speech” is sufficient [Ido Dagan; Seneval’04]

How about Collin Powel? In many disambiguation problems, the view taken

when studying entailment is that keeping the underspecificity of language is possible, and perhaps the right thing to do.

Example: New (Better?) View on Problems

Task-based Refinement

An unified framework to study Learning, Knowledge Representation and Reasoning.

A series of theoretical results on the advantages of a unified framework for L, KR & R, in a situations where:

The goal is to Reason - deduction; abduction (best explanation)

Starting point for Reasoning is not a static Knowledge Base but rather A representation of knowledge learned via interaction with the world.

Quality of the learned representation is determined by the reasoning stage.

Intermediate Representation is important – but only to the extent that it is learnable, and it facilitates reasoning.

There may not be a need (or even a possibility) to learn an exact intermediate representation, but only to the extent that is supports Reasoning.

[Khardon & Roth JACM97, AAAI94; Roth95, Roth96, Khardon&Roth99Learning to Plan: Khardon’99]

Learning in order to Reason [’94-’97]

Lesson:

Reflection from the Past




Conclusions

This Talk

Mapping text to a canonical representation is often not the right approach (or: not possible) Not a computational issue Rather, the representation might depend on the task, in

our case, on the hypothesis sentence.

Suggests a definition for textual entailment:

Let s, t, be text snippets with representations rs, rt 2 R.

We say that s textually entails t if there is a representation

r 2 R of s, for which we can prove that r µ rt

Defining Textual Entailment

R - a knowledge representation language, with a well defined

syntax and semantics or a domain D.

For text snippets s, t: rs, rt - their representations in R. M(rs), M(rt) their model theoretic representations

There is a well defined notion of subsumption in R, defined model theoretically

u, v 2 R: u is subsumed by v when M(u) µ M(v)

Not an algorithm; need a proof theory.

Defining Semantic Entailment

The proof theory is weak; will show rs µ rt only when they are relatively “similar”.

r 2 R is faithful to s if M(rs) = M(r)

Definition: Let s, t, be text snippets with representations rs, rt 2 R.

We say that s textually entails t if there is a representation r 2 R that is faithful to s, for which we can prove that r µ rt

Given rs one needs to generate many equivalent representations r’s and test r’s µ rt

Defining Semantic Entailment (2)

Cannot be done exhaustively How to generate alternative representations?

A rewrite rule (l,r) is a pair of expressions in R such that l µ r

Given a representation rs of s and a rule (r,l) for which rs µ l the augmentation of rs via (l,r) is r’s = rs Æ r.

Claim: r’s is faithful to s.

Proof: In general, since r’s = rs Æ r then M(r’s)= M(rs) Å M(r) However, since rs µ l µ r then M(rs) µ M(r).

Consequently: M(r’s)= M(rs)

And the augmented representation is faithful to s.

The Role of Knowledge: Refining Representations

rs l µ r, rs µ lµ

r’s = rs Æ r

The claim suggests an algorithm for generating alternative (equivalent) representations, and for textual entailment.

The resulting algorithm is sound, but is not complete. Completeness depends on the quality of the KB of rules.

The power of this re-representation algorithm is in the rules KB and in an inference procedure that incorporates them.

Choosing appropriate refinements Depends on the target sentence Is an optimization procedure

.

Comments

General Strategy

Given a sentence S (answer)

Find the optimal set of transformations that maps one sentence to the target sentence.

Given a KB of semantic; structural

and pragmatic transformations

(rules).

Given a sentence T (question)

e Induce an abstract representation of S (a

concept graph)

Induce an abstract representation of S (a

concept graph)

Re-represent S

Re-represent S

Cartoon

Inducing an Abstract Representation of Text Multiple learning Steps; centered around a semantic parse (predicate-

argument representation) of a sentence augmented by additional information.

Final representation is a hierarchical concept graph (DL inspired) Refining the representation using an existing KB

Rewrite rules at multiple levels; application depends on target; [Features] Modeling Entailment as Constrained Optimization

Entailment is a mapping between sentence representation Find an optimal mapping [minimal cost proof; abduction] that

respects The hierarchy Transformations (rules) applied to nodes/edges/sub-graphs The confidence in the induced information

All modeled as (soft) constraints Provides robustness against inherent variability in natural

language, inevitable noise in learning processes and missing information.

The One Slide Approach Summary

Learning, Representing and Reasoning take part at several levels in the process.

A unified knowledge representation of the text, that provides an hierarchical encoding of the structural,

relational and semantic properties of the given text

is integrated with learning mechanisms that can be used to induce such information from newly observed raw text, and

that is equipped with an inferential mechanism that can be used to support inferences with respect to such representations.

An Inference Model for Semantic Entailment [AAAI’05] Experiments with a Semantic Entailment System [IJCAI’05-WS]

Components

An Example

s: Lung cancer put an end to the life of Jazz singer Marion Montgomery on Monday.

t: Singer dies of carcenoma.

s is re-represented in several ways; one of these is shown to be subsumed by t

s’1: Lung cancer killed Jazz singer Marion Montgomery on Monday.

s’2: Jazz singer Marion Montgomery died of lung cancer on Monday.

Representation Hierarchical; Multiple types of information;All hanging on the sentence itself.

Formally, represented using Description Logic Expressions; Rewrite rules have the same representation.

Representation (2)

Representation is formal – not to be confused with a logical/canonical representation.

Attempt is made to represent the text, and augment/refine the representation as part of the inference process.

The skeleton of the representation is a predicate-argument representation

learned based on PropBank (the semantic role labelling task).

Resources used to augment the representation:

Segmentation; tokenization; Lemmatizer;POS tagger Shallow Parser Syntactic parser (Collins;Charniak) Named entity tagger Entity identification. (co-Reference)

Resources used to Rewrite/Refine and for Subsumption

Wordnet Dirt paraphrase rules (Lin) Word clusters (Lin) Ad hoc modules (later)

In house machine learning based tools [http://L2R.cs.uiuc.edu/~cogcomp

Predicate-Argument Representation

For each predicate in a sentence [currently – verbs] Represent all constituents that fill a semantic role

• Core Arguments, e.g., Agent, Patient or Instrument• Their adjuncts, e.g., Locative, Temporal or Manner

I left my pearls to my daughter-in-law in my will.

A0 : leaver

A1 : thing left

A2 : benefactor

AM-LOC

The pearls which I left to my daughter-in-law are fake.

A0 : leaver

A1 : thing left

A2 : benefactor

R-A1

The pearls, I said, were left to my daughter-in-law.

A0 : sayer

A1 : utterance

C-A1 : utterance

Screen shot from a CCG demo http://L2R.cs.uiuc.edu/~cogcomp

This problem itself is modelled as a constrained optimization problem over the output of a large number of classifiers, and multiple constraints.

Solution: formulating it as a linear program and solving integer linear programs.

Top system in CoNLL shared Task; presentation later today

Semantic Role Labelling

Rewrite Rules (KB)

Goal: Acquire transformations that preserve meaning Basic linguistics processing levels:

Keyword matching; Grammatical; Semantic; (Discourse, Pragmatic, …)

The mechanism supports chaining. Rules may contain variables; the augmentation mechanism supports inheritance.

Some examples later Rules are used also to avoid semantic parsing

problems. managed to enter entered; failed to enterenternot

The Inference Problem

1. Optimizing over the transformations applied to the initial representation.

2. Optimizing over the transformations applied to determine final subsumption Even after the refinement of the representation,

requiring exact subsumption (embedding of the target graph in the source graph) is unrealistic.

Words can be replaced by synonyms; modifiers can be dropped, etc.

We develop a notion of functional subsumption: say “yes” when node & edges unify modulo some allowed transformations.

[Why do we separate to two stages?]

1. Incrementally augment the original representation and generate faithful re-representations of it.

2. Compute whether the target representation subsumes the augmented concept graph via an extended subsumption algorithm.

Uncertainty is encoded by optimizing a linear cost function. Cost can be learned in a straight forward way via and EM-like algorithm.

The inference model seeks the optimal re-representation S'i such that:

S'i = argmin{S‘ | C(S,S'i) + D(S'i,T) }

Over the space of all possible re-representations of S given KB (subject to multiple constraints – order, structure)

C returns the cost of augmenting S to S'i and D returns the costs of performing extended subsumption from S'i to T.

Modeling Inference as Optimization

Inference: Key Points

Hierarchical Subsumption Decision List: if succeeds at a level, go on to the next;

otherwise, fail At the Predicate-Argument level At the phrase level At the word level

Match both attributes and edges (relational information) Match may not be perfect

Inference (unification) as Optimization The optimal unification U’ is the one minimizing:

Hi {(X,Y) U| X Hi} iG (X,Y) (X,Y, resp. substructures on S, T)

where i is a fixed constant that ensures the hierarchical behavior is as a decision list.

(i makes sure that changes in H0 dominate changes in H1)

Integer Linear Programming formulation for Unification

KR: [Learning & Inference]

A description logic inspired hierarchical KR into which we re-represent the surface level text augmented with multiple abstractions.

KB: [Acquisition & Inference]

A knowledge base consisting of syntactic and semantic rewrite rules, written at several levels of abstractions

Inference: [modeled as optimization: flexibility & error tolerance]

An extended subsumption algorithm which determines subsumption between representations. An Inference Model for Semantic Entailment [AAAI’05]

Experiments with a Semantic Entailment System [IJCAI’05-WS] Evaluation: SRL (CoNLL Shared Task) ; Pascal

Ablation study on the PARC collection

Summary




Conclusions

This Talk

PARC Data 76 Pairs of Q-A sentences

questions converted manually treat label “unknown” as “false”

Designed to test linguistic (lexical and constructional) entailment

Out of 76 pairs: 64 pairs – got perfect SRL labelling

System versions: Vary Two Dimensions Structure: add more parsing capabilities Semantic: add more semantic resources (some use

parse structure)

Ablation study on the PARC Data

System Versions

Suite of tests, incrementally adding system components

System versions: LLM: Uses BOW++ to match entire sentences SRL + LLM: Uses SRL tagging (filter) and BOW on

verb arguments SRL + Deep Structure: System parses arguments

of Verbs Uses full parse, shallow parse tagging to identify

argument structure Knowledge Base (of rewrite rules) active or

inactive

Testing the Entailment System

Entailment (Knowledge Base) Modules (can only be activated when appropriate parse structure is present)

Verb Phrase Compression Rewrite verb constructions – modal, VERB to VERB, tense

Discourse Analysis Detect embedded predicates Annotate effect of embedding predicate on embedded

predicate Qualifier Reasoning

Detect qualifiers and scope – some, no, all, any, etc. Determine entailment of qualified arguments

Not shown: Functional Subsumption – rules (e.g., synonyms) used to allow other rules to fire.

Perfect Corpus with applicable entailment modules, with Knowledge Base

Active Components

System Base Base + VP

Base + VP + DA

Base + VP + DA + Qual

LLM 60.94 N/A N/A N/A

SRL+LLM

59.38 65.63 N/A N/A

SRL + Deep Structure

68.75 75.00 81.25 82.81

Results for Different Entailment Systems

Full Corpus with applicable entailment modules, with Knowledge Base

Active Components


Base + VP + DA



SRL+LLM

57.89 61.84 N/A N/A


65.79 68.42 76.32 77.63


Baseline Entailment System (1) *

Baseline system is Lexical Level Matching (LLM) Ignores many “stopwords”, including “be” verbs,

prepositions, determiners Lemmatizes words before matching

Requiring structure may hurt: LLM allows entailment when SRL-based subsumption requires a rewrite rule:

For LLM, the only words of T that register are ”diplomat” and “Iraq”

As these are present in S, LLM will return “true”

S: [The diplomat]/ARG1 visited [Iraq]/ARG1 [in September]/AM_TMP

T: [The diplomat]/ARG1 was in [Iraq]/ARG2

Baseline System (1.1) *

But, LLM is insensitive to small changes in wording

LLM ignores modal “could”, so returns incorrect answer “true”.

S: [Legally]/AM_ADV, [John]/ARG0 [could]/AM_MOD drive.

T: [John]/ARG0 drove.

SRL + LLM (2.)

SRL + LLM system uses Semantic Role Labeler tagging First, tries to match verb and argument types in the two

sentences If successful, system uses LLM to determine entailment of

arguments Advantage over LLM when argument or modifier attached to

different verb in T than in S:

Words are identical, so LLM incorrectly labels example “true” SRL+LLM returns “false” because arguments of “said”,

“visit” don’t match.

S: [The president]/ARG0 said [[the diplomat]/ARG0 left [Iraq]/ARG1]/ARG1

T: [The diplomat]/ARG0 said [[the president]/ARG0 left [Iraq]/ARG1]/ARG1

SRL + LLM (2.1) *

Disadvantage of using SRL+LLM compared to LLM: SRL generates predicate frames verbs ignored as stopwords

by LLM Example: “went” in following sentence pair:

LLM ignores “went”, returns correct label “true” SRL generates a verb frame for “went”

Subsumption fails as no match for this verb in S In this data set, more instances like the second case than

like the first the result is a drop in performance However, SRL forms crucial backbone for other functionality

S: [The president]/ARG0 visited [Iraq]/ARG1 [in September]/AM_TMP

T: [The president]/ARG0 went to [Iraq]/ARG1.

SRL+LLM with Verb Processing (3.0) *

The Verb Processing (VP) module rewrites certain verb phrases as a single verb with additional attributes

Uses word order and Part of Speech information to identify candidate patterns

Presently recognizes modal and tense constructions, and simple verb compounds of the form ”VERB to VERB” (such as “manage to enter”)

Verb phrase replaced by single predicate (verb) node with additional attributes

Modality (“CONFIDENCE”) Tense

Requires POS and word order information Default CONFIDENCE is “FACTUAL”

SRL+LLM with Verb Processing (3.1) *

Example where Verb Processing (VP) module helps:

Subsumption in LLM and SRL+LLM system succeeds, as argument and verb lemma in T match those in S

VP module rewrites “could drive” as “drive”, adds attribute “CONFIDENCE: POTENTIAL” to “drive” predicate node

In SRL+LLM+VP, subsumption fails at verb level, as CONFIDENCE attributes don’t match

S: [Legally]/AM_ADV, [John]/ARG0 [could]/AM_MOD drive.

T: [John]/ARG0 drove.

SRL+LLM with Verb Processing (3.2)

VP module rewrites auxiliary construction in T as a

single verb with tense and modality attributes attached

Now, SRL generates only a single predicate frame for “sold”

This matches its counterpart in S, and subsumption succeeds,

qualifying effect of the verb ``said'' in S cannot be recognized without the deeper parse structure and the Discourse Analysis module.

S: Bush said that Khan sold centrifuges to North Korea.

T: Centrifuges sold to North Korea.

SRL + Deep Structure (4.0) *

SRL + Deep Structure entailment system identifies substructure in SRL predicate arguments

uses full- and shallow parse, Named Entity and Part of Speech information

identifies the key entity in each argument Identifies modifiers of key entity such as

adjectives, titles, and quantities Enables further semantic modules, such as

Qualifier module for reasoning about entailment of qualified arguments

SRL + Deep Structure (4.0) *

“Some” and “no” are stopwords (i.e., ignored by LLM), so LLM and SRL+LLM incorrectly label this example “true”

SRL + Deep Structure gives correct label, “false”, because “no” and “some” are identified as key entity modifiers for matching argument, and they don’t match

S: No US congressman visited Iraq until the war.

T: Some US congressmen visited Iraq before the war.

SRL + Deep Structure (4.2)

Handling modifiers:

No rules for modifiers: The LLM and SRL+LLM systems find no match for “intelligent” in S, and so return the correct answer, “false”

SRL + Deep Structure system allows unbalanced T adjective modifiers (assumption: S must be more general than T) and returns “true”.

Context sensitive handling of modifiers?

S: The room was full of women.

T: The room was full of intelligent women.

SRL + Deep Structure + Discourse Analysis (5.0) *

Detecting the effects of an embedding predicate on the embedded predicate

Presently, supports distinction between “FACTUAL” (default assumption) and a set of values that distinguish various types of uncertainty, such as “REPORTED”

All systems lacking Discourse Analysis (DA) module label this sentence pair “true”, because T is a literal fragment of S

Actual truth value depends on interpretation of “reported”

Other embedding constructions DA can handle: Adjectival: “It is unlikely that Hanssen sold secrets…” Nominal: “There was a suspicion that Hanssen sold secrets…”

S: The New York Times reported that Hanssen sold FBI secrets to the Russians and could face the death penalty.

T: Hanssen sold FBI secrets to the Russians.

SRL + Deep Structure + DA + Qualifier (6.0) *

The Qualifier module allows comparison of qualifiers such as all, some, many, no, etc.

In the following example it is used to identify that “all soldiers” entails “many soldiers”

S: All soldiers were killed in the ambush.

T: Many soldiers were killed in the ambush.

Perfect Corpus with applicable entailment modules, with Knowledge Base

Active Components


Base + VP + DA



SRL+LLM

59.38 65.63 N/A N/A


68.75 75.00 81.25 82.81


Full Corpus with applicable entailment modules, with Knowledge Base

Active Components


Base + VP + DA



SRL+LLM

57.89 61.84 N/A N/A


65.79 68.42 76.32 77.63


Experiment: Conclusions

Monotonic improvement as additional analysis resources are added.

Best performance for system with most structural information (which supports the most semantic analysis modules)

Non-monotonic improvement, relative to LLM, because: LLM robust to certain errors due to stopwords SRL matching stricter: fewer false positives, more false

negatives Corpus distribution favors LLM

Consistent behavior for “imperfect” corpus (includes SRL errors)

Hierarchical representational approach shows strong promise

Summary

Progress in Natural Language Understanding requires the ability to learn, represent and reason with respect to structured and relational data.

The task of Textual Entailment provides a general setting within which to study and develop these theories. At the same time, it supports some immediate applications.

We argued for an approach that Attempts to refine a learned representation using a

collection of knowledge modules, thus maintaining some of the under specificity in language as far as possible.

Models inference as an optimization problem that attempts to find the minimal cost solution.

No surprise, the key issues in this approach are in knowledge acquisition.

1 knowledge representation and inference models for textual entailment dan roth university of...

Documents

fundamental task walmart

book winnie

separate fundamental

multiple nlp

multiple issues

overture entails

multiple direct applications

multiple tasks