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The PASCALRecognizing Textual Entailment

Challenges - RTE-1,2,3

Ido Dagan Bar-Ilan University, Israelwith …

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Recognizing Textual Entailment

PASCAL NOE Challenge2004-5

Ido Dagan, Oren glickman Bar-Ilan University, IsraelBernardo Magnini ITC-irst, Trento, Italy

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The Second PASCAL Recognising Textual Entailment Challenge

Roy Bar-Haim, Ido Dagan, Bill Dolan, Lisa Ferro, Danilo Giampicollo, Bernardo Magnini, Idan Szpektor

Bar-Ilan, CELCT, ITC-irst, Microsoft Research, MITRE

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The Third Recognising Textual Entailment

Challenge

Danilo Giampiccolo (CELCT) and Bernardo Magnini (FBK-ITC)

With Ido Dagan (Bar-Ilan) and Bill Dolan (Microsoft Research) Patrick Pantel (USC-ISI), for Resources Pool

Hoa Dang and Ellen Voorhees (NIST), for Extended Task

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RTE Motivation

• Text applications require semantic inference• A common framework for addressing applied

inference as a whole is needed, but still missing– Global inference is typically application dependent– Application-independent approaches and resources exist

for some semantic sub-problems

• Textual entailment may provide such common application-independent semantic framework

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Framework Desiderata

A framework for modeling a target level of language processing should provide:

1) Generic module for applications– A common underlying task, unified interface (cf.

parsing)

2) Unified paradigm for investigating sub-phenomena

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Outline

• The textual entailment task – what and why?

• Evaluation dataset & methodology

• Participating systems and approaches

• Potential for machine learning

• Framework for investigating semantics

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Natural Language and Meaning

Meaning

Language

Ambiguity

Variability

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Variability of Semantic Expression

Model variability as relations between text expressions:

• Equivalence: text1 text2 (paraphrasing)• Entailment: text1 text2 – the general case

Dow ends up

Dow climbs 255

The Dow Jones Industrial Average closed up 255

Stock market hits a record high

Dow gains 255 points

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Typical Application Inference

Overture’s acquisition by Yahoo

Yahoo bought Overture

Question Expected answer formWho bought Overture? >> X bought Overture

• Similar for IE: X buy Y

• “Semantic” IR: t: Overture was bought …

• Summarization (multi-document) – identify redundant info

• MT evaluation (and recent ideas for MT)

• Educational applications, …

text hypothesized answer

entails

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KRAQ'05 Workshop - KNOWLEDGE and REASONING for ANSWERING QUESTIONS

(IJCAI-05)

CFP:– Reasoning aspects:

    * information fusion,    * search criteria expansion models     * summarization and intensional answers,    * reasoning under uncertainty or with incomplete

knowledge,– Knowledge representation and integration:

    * levels of knowledge involved (e.g. ontologies, domain knowledge),

    * knowledge extraction models and techniques to optimize response accuracy

… but similar needs for other applications – can entailment provide a common empirical task?

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Classical Entailment Definition

• Chierchia & McConnell-Ginet (2001):A text t entails a hypothesis h if h is true in every circumstance (possible world) in which t is true

• Strict entailment - doesn't account for some uncertainty allowed in applications

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“Almost certain” Entailments

t: The technological triumph known as GPS … was incubated in the mind of Ivan Getting.

h: Ivan Getting invented the GPS.

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Applied Textual Entailment• Directional relation between two text

fragments: Text (t) and Hypothesis (h):

t entails h (th) if humans reading t will infer that h is most likely true

• Operational (applied) definition:– Human gold standard - as in NLP applications– Assuming common background knowledge – which

is indeed expected from applications

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Evaluation Dataset

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Generic Dataset by Application Use

• 7 application settings in RTE-1, 4 in RTE-2/3– QA – IE– “Semantic” IR– Comparable documents / multi-doc summarization– MT evaluation– Reading comprehension – Paraphrase acquisition

• Most data created from actual applications output• ~800 examples in development and test sets• 50-50% YES/NO split

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Some Examples

TEXTHYPOTHESISTASKENTAIL-

MENT

1Regan attended a ceremony in Washington to commemorate the landings in Normandy.

Washington is located inNormandy.

IEFalse

2Google files for its long awaited IPO.Google goes public.IRTrue

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…: a shootout at the Guadalajara airport in May, 1993, that killed Cardinal Juan Jesus Posadas Ocampo and six others.

Cardinal Juan Jesus Posadas Ocampo died in 1993.

QATrue

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The SPD got just 21.5% of the votein the European Parliament elections,while the conservative opposition partiespolled 44.5%.

The SPD is defeated by

the opposition parties.IETrue

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Final Dataset (RTE-2)• Average pairwise inter-judge agreement: 89.2%

– Average Kappa 0.78 – substantial agreement– Better than RTE-1

• Removed 18.2% of pairs due to disagreement (3-4 judges)

• Disagreement example:– (t) Women are under-represented at all political levels ...

(h) Women are poorly represented in parliament.

• Additional review removed 25.5% of pairs– too difficult / vague / redundant

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Final Dataset (RTE-3)

• Each pair judged by three annotators

• Pairs on which the annotators disagreed were filtered-out.

• Average pairwise annotator agreement: 87.8% (Kappa level of 0.75)

• Filtered-out pairs:– 19.2 % due to disagreement– 9.4 % as controversial, too difficult, or too similar to

other pairs

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Progress from 1 to 3

• More realistic application data:– RTE-1: some partly synthetic examples– RTE-2&3 mostly:

• Input from common benchmarks for the different applications• Output from real systems

– Test entailment potential across applications• Text length:

– RTE-1&2: one-two sentences– RTE-3: 25% full paragraphs, requires discourse modeling/anaphora

• Improve data collection and annotation– Revised and expanded guidelines– Most pairs triply annotated, some across organizers sites

• Provide linguistic pre-processing, RTE Resources Pool• RTE-3 pilot task by NIST: 3-way judgments; explanations

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Suggested Perspective

RE the Arthur Bernstein competition:

“… Competition, even a piano competition, is legitimate … as long as it is just an anecdotal side effect of the musical culture scene, and doesn’t threat to overtake the center stage”

Haaretz Israeli News Paper,Culture Section, April 1st, 2005

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Participating Systems

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Participation

• Popular challenges, world wide:– RTE-1 – 17 groups – RTE-2 – 23 groups – RTE-3 – 26 groups

• 14 Europe, 12 US

• 11 newcomers (~40 groups so far)

• 79 dev-set downloads (44 planned, 26 maybe)

• 42 test-set downloads

• Joint ACL-07/PASCAL workshop (~70 participants)

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Methods and Approaches• Estimate similarity match between t and h

(coverage of h by t): – Lexical overlap (unigram, N-gram, subsequence)– Lexical substitution (WordNet, statistical)– Lexical-syntactic variations (“paraphrases”)– Syntactic matching/edit-distance/transformations– Semantic role labeling and matching– Global similarity parameters (e.g. negation, modality)– Anaphora resolution

• Probabilistic tree-transformations• Cross-pair similarity• Detect mismatch (for non-entailment)• Logical interpretation and inference

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Dominant approach: Supervised Learning

• Features model various aspects of similarity and mismatch• Classifier determines relative weights of information sources• Train on development set and auxiliary t-h corpora

t,hSimilarity Features:

Lexical, n-gram,syntacticsemantic, global

Feature vector

Classifier

YES

NO

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Parse-based Proof Systems

rainverb

whenadj

leaveverb

whaexpletive

ROOTi

itotheri

Marynoun

Johnnoun

subj

conj

N2noun

N1nounconjN2noun

It rained when John and Mary left

itother

rainverb

whenadj

leaveverb

wha

ROOTi

i

Marynoun

subj

leftverb

ROOT

i

Marynoun

subj

V1verb

whenadj

ROOTi

i

V2verb

ROOT

V2verb

i

wha

It rained when Mary left

Mary left

expletive

(Bar-Haim et al., RTE-3)

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Resources• WordNet, Extended WordNet, distributional similarity

– Britain UK

– steal take

• DIRT (paraphrase rules)– X file a lawsuit against Y X accuse Y (world knowledge)

– X confirm Y X approve Y (linguistic knowledge)

• FrameNet, ProBank, VerbNet– For semantic role labeling

• Entailment pairs corpora– Automatically acquired training

• No dedicated resources for entailment yet

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Accuracy Results – RTE-1

0.4

0.5

0.6

MIT

RE

Bar

Ila

nU

NE

DD

ublin

Edi

nbur

gh-

Dub

linS

tanf

ord

UIU

CIR

ST

IRS

TU

NE

DE

dinb

urgh

-A

mst

erda

mS

tanf

ord

LCC

Am

ster

dam

accuracy

0.01 sig

0.05 sig

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Results (RTE-2)

First Author (Group)AccuracyAverage Precision

Hickl (LCC)75.4%80.8%

Tatu (LCC)73.8%71.3%

Zanzotto (Milan & Rome)63.9%64.4%

Adams (Dallas)62.6%62.8%

Bos (Rome & Leeds)61.6%66.9%

11 groups58.1%-60.5%

7 groups52.9%-55.6%

Average: 60%Median: 59%

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Results: RTE-3

Accuracy1. Hickl - LCC0.80

2. Tatu - LCC0.72

3. Iftene - Uni. Iasi0.69

4. Adams - Uni. Dallas0.67

5. Wang - DFKI0.66

Baseline (all YES)0.51

Two systems above 70%

Most systems (65%) in the range 60-70%; they were just 30% at RTE-2

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Current Limitations

• Simple methods perform quite well, but not best• System reports point at:

– Lack of knowledge (syntactic transformation rules, paraphrases, lexical relations, etc.)

– Lack of training data

• It seems that systems that coped better with these issues performed best:– Hickl et al. - acquisition of large entailment corpora for

training– Tatu et al. – large knowledge bases (linguistic and world

knowledge)

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Impact

• High interest in the research community– Papers, conference sessions and areas, PhD theses,

funded projects– Special issue - Journal of Natural Language Engineering– ACL-07 tutorial

• Initial contribution to specific applications– QA – Harabagiu & Hickl, ACL-06; CLEF-06/07– RE – Romano et al., EACL-06

• RTE-4 – by NIST, with CELCT– Within TAC, a new semantic evaluation conference

(with QA and summarization, subsuming DUC)

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New Potentials for Machine Learning

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Classical Approach = Interpretation

Stipulated Meaning

Representation(by scholar)

Language(by nature)

Variability

Logical forms, word senses, semantic roles, named entity types, … - scattered tasks

Feasible/suitable framework for applied semantics?

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Textual Entailment = Text Mapping

Assumed Meaning (by humans)

Language(by nature)

Variability

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General Case – Inference

MeaningRepresentation

Language

Entailment mapping is the actual applied goal - and also a touchstone for understanding!

Interpretation becomes a possible mean

Inference

Interpretation

Textual Entailment

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Machine Learning Perspectives

• Issues with interpretation approach:– Hard to agree on target representations– Costly to annotate semantic representations for training– Has it been a barrier?

• Language-level entailment mapping refers to texts– Texts are semantic-theory neutral– Amenable for unsupervised/semi-supervised learning

• It would be interesting to explore (many do) – language-based representations of meaning, inference knowledge,

and ontology,– for which learning and inference methods may be easier to develop.– Artificial intelligence through natural language?

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Major Learning Directions

• Learning entailment knowledge (!!!)– Learning entailment relations between words/expressions

– Integrating with manual resources and knowledge

• Inference methods– Principled frameworks for probabilistic inference

• Estimate likelihood of deriving hypothesis from text

• Fusing information levels

– More than bags of features

• Relational learning relevant for both• How can we increase ML researchers involvement?

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Learning Entailment Knowledge

• Entailing “topical” terms from words/texts– E.g. medicine, law, cars, computer security, …

– An unsupervised version of text categorization

• Learning entailment graph for terms/expressions– Partial knowledge: statistical, lexical resources, Wikipedia, …

– Estimate link likelihood in context

acquire/v

own/v

acquisition/n

buy/v

purchase/n

derived

WN-syn

Dist. sim

entails

entails? ?

?

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Meeting the knowledge challenge – by a coordinated effort?

• A vast amount of “entailment rules” needed• Speculation: can we have a joint community

effort for knowledge acquisition?– Uniform representations

– Mostly automatic acquisition (millions of rules)

– Human Genome Project analogy

• Preliminary: RTE-3 Resources Pool at ACLWiki(set up by Patrick Pantel)

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Textual Entailment ≈ Human Reading Comprehension

• From a children’s English learning book(Sela and Greenberg):

Reference Text: “…The Bermuda Triangle lies in the Atlantic Ocean, off the coast of Florida. …”

Hypothesis (True/False?): The Bermuda Triangle is near the United States

???

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Where are we (from RTE-1)?

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Cautious Optimism

1) Textual entailment provides a unified framework for applied semantics

– Towards generic inference “engines” for applications

2) Potential for:– Scalable knowledge acquisition,

boosted by (mostly unsupervised) learning

– Learning-based inference methods

Thank you!

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Summary: Textual Entailment as Goal

• The essence of our proposal: Base applied inference on entailment “engines” and KBsFormulate various semantic problems as entailment tasks

• Interpretations and “mapping” methods may compete/complement

• Open question: which inferences– can be represented at language level?

– require logical or specialized representation and inference? (temporal, spatial, mathematical, …)

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Collecting QA Pairs• Motivation: a passage containing the answer slot

filler should entail the corresponding answer statement.– E.g. for: Who invented the telephone?, and answer Bell,

text should entail Bell invented the telephone

• QA systems were given TREC and CLEF questions.

• Hypothesis generated by “plugging” the system answer term into the affirmative form of the question

• Texts correspond to the candidate answer passages

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Collecting IE Pairs• Motivation: a sentence containing a target

relation instance should entail an instantiated template of the relation– E.g: X is located in Y

• Pairs were generated in several ways– Outputs of IE systems:

• for ACE-2004 and MUC-4 relations

– Manually:• for ACE-2004 and MUC-4 relations

• for additional relations in news domain

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Collecting IR Pairs

• Motivation: relevant documents should entail a given “propositional” query.

• Hypotheses are propositional IR queries, adapted and simplified from TREC and CLEF– drug legalization benefits

drug legalization has benefits

• Texts selected from documents retrieved by different search engines

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Collecting SUM (MDS) Pairs• Motivation: identifying redundant statements

(particularly in multi-document summaries)• Using web document clusters and system summary• Picking for hypotheses sentences having high lexical

overlap with summary• In final pairs:

– Texts are original sentences (usually from summary)– Hypotheses:

• Positive pairs: simplify h until entailed by t• Negative pairs: simplify h similarly

• In RTE-3: using Pyramid benchmark data