Interlingua-based MT

Interlingua-based Machine Translation Syntactic transfer-based MT – Couples the syntax of the two languages What if we abstract away the syntax – All that remains is meaning – Meaning is the same across languages – Simplicity: Only N components needed to translate among N languages Two “small” problems: – What is meaning? – How do we represent meaning? Direct MT Interlingua Transfer-based MT Source Target Parsing Semantic Interpretation Semantic Generation Syntactic Generation Syntactic Structure English analyzer Spanish analyzer Japanese analyzer Spanish Generator Japanese Generator English generator Interlingual representation

Example of Interlingua Machine Translation need Imake tocall a collect 必要があります (need) 私は (I) かける (make) コールを (call) コレクト (collect) Interlingua representation

Ingredients of a semantic representation language neutral – Syntactic variations should result is the same semantics sense of a word deep semantic role labels scope of quantifiers, adverbials, adjectives polarity information Distinguish between surface structure (syntactic structure) and deep structure (semantic structure) of sentences. Different forms of semantic representation: logic formalisms ontology / semantic representation languages Case Frame Structures (Filmore) Conceptual Dependy Theory (Schank) Description Logic (DL) and similar KR languages Ontologies

Text Meaning Representation Lexicon has two components – Syntactic part – Semantic constraints part Given a sentence, the syntactic part analyzes the input syntactically and the semantic constraints create semantic expressions that can be evaluated. Ontology specifies the type hierarchy – Used for checking selectional restrictions – Selectional restrictions used for word-sense disambiguation e.g. accident is an event; organization has humans

Constructing a Semantic Representation General approach: Start with surface structure derived from parser. Map surface structure to semantic structure:  Use phrases as sub-structures.  Find concepts and representations for central phrases (e.g. VP, NP, then PP)  Assign phrases to appropriate roles around central concepts (e.g. bind PP into VP representation).

Semantic Representation Semantic Representations are based on some form of (formal) Representation Language. Semantics Networks Conceptual Dependency Graphs Case Frames Ontologies DL and similar KR languages Important note: Difference between relations between text strings and referents in the world.

Ontology (Interlingua) approach Ontology: a language-independent classification of objects, events, relations A Semantic Lexicon, which connects lexical items to nodes (concepts) in the ontology An analyzer that constructs Interlingua representations and selects an appropriate one

Semantic Lexicon Provides a syntactic context for the appearance of the lexical item Provides a mapping for the lexical item to a node in the ontology (or more complex associations) Provides connections from the syntactic context to semantic roles and constraints on these roles

Constructing an InterLingua Representation For each syntactic analysis: Access all semantic mappings and contexts for each lexical item. Create all possible semantic representations. Test them for coherency of structure and content.

Input: John makes tools Syntactic Analysis: Basic Semantic Dependency - Example catverb root make tensepresent subject root john catnoun-proper object root tool catnoun numberplural

John-n1 syn-struc rootjohn catnoun-proper sem-struc human name john gender male tool-n1 syn-struc roottool catn sem-struc tool Lexicon Entries for John and tool

Relevant extract from the specification of the ontological concept used to describe the appropriate meaning of make: manufacturing-activity... agenthuman themeartifact … Ontological Representation - Example who what

The basic semantic dependency component of the “Text Meaning Representation” (TMR) for: John makes tools manufacturing-activity-7 agenthuman-3 themeset-1 element tool cardinality> 1 … Semantic Dependency Component

try-v3 syn-struc root try cat v subj root $var1 cat n xcomp root $var2 cat v formOR infinitive gerund sem-struc set-1element-typerefsem-1 cardinality>=1 refsem-1 semevent agent^$var1 effectrefsem-2 modality modality-typeepiteuctic modality-scoperefsem-2 modality-value< 1 refsem-2value^$var2 semevent Means “non finished action; outcome unclear” semantic representation of “try-v3”

“Why is Iraq developing weapons of mass destruction?”

Word sense Disambiguation Methods  Constraint checking  make sure the constraints imposed on context are met  Graph traversal  is-a links are inexpensive  other links are more expensive  the “cheapest” structure is the most coherent one  Hunter-gatherer processing  find (hunt) and eliminate (kill) unlikely interpretations  collect (gather) remaining interpretations

Ontological Semantics: An example semantic representation language slides from S. Nirenberg

Ontological semantics is a computationally tractable theory of meaning in natural language as well as a suite (OntoSem) of implemented NLP programs and a set of static knowledge resources that support these programs. Ontological semantics deals directly with extraction, representation and manipulation of text meaning. Ontosem text analyzers produce interpreted knowledge ready to be used in reasoning-heavy applications that include question answering, cross-document and cross-lingual text summarization, question answering, machine translation and others. Support of intelligent human-computer interaction in domain- and task-oriented environments is squarely within the purview of ontological semantics.

Ontological semantics concentrates on content of representations and is adaptable to a number of different representation formats. Ontological semantics is both a producer and a consumer of knowledge: deriving text meaning is itself a knowledge-intensive task

OntoSem is devoted to processing naturally occurring texts strives for high-quality results first followed by concern for broad coverage expects “unexpected” inputs seeks quality heuristics of any provenance (knowledge- based or probabilistic, cooccurrence-based) does not grant syntax a privileged position among the providers of heuristics for semantic processing does not make a strong distinction between semantics and pragmatics is applicable to any natural language

Ontological-semantic analyzers take natural language texts as inputs and generate machine- tractable text meaning representations (TMRs) that form the basis of various reasoning processes. Sample Input Sentence: Iran, Iraq and North Korea on Wednesday rejected an accusation by President Bush that they are developing weapons of mass destruction. The TMR (presented graphically) for the above is as follows:

Output: A Text Meaning Representation (TMR) This presentation is simplified; the system, in fact, derives much more from text; event instances are shown in ellipses; object instances, in rectangles; only case role and set membership relations are shown (as labels on links); numerical constraints can be fuzzy, as in the cardinality of SET-1226.

Word Sense Disambiguation Instances of Ontological Concepts Semantic Dependencies (fillers of ontological properties mentioned in text; not simply relations among textual strings) Triggers for further context- dependent processing Many additional properties stored with concepts underlying instances A pretty-printed fragment of the actual TMR representation for sample input

Ontological-semantic systems centrally rely on the following static knowledge resources: a language-independent ontology that includes knowledge about types of entities in the world, e.g., ATHLETE, WELD or SPEED; ontology-oriented lexicons (and onomasticons, or lexicons of proper names) for each natural language in the system; and a fact repository containing instances of ontological concepts, e.g., Andre Agassi (ATHLETE-3176) or the Apollo 13 mission (SPACEFLIGHT-142)

A Sample Screen of the Ontology/Lexicon/Fact Repository Browsing and Editing Environment

(diagnosis (diagnosis-n1 (cat n) (anno (def "") (ex "The diagnosis (of cancer) (by the specialist) was made quickly") (comments "")) (syn-struc ((root $var0) (cat n) ; diagnosis (pp-adjunct ((root of) (root $var1) (cat prep) (opt +); of (obj ((root $var2) (cat n))))); disease (pp-adjunct ((root by) (root $var3) (cat prep) (opt +); by (obj ((root $var4) (cat n))))))); someone (sem-struc (DIAGNOSE ; the ontological mapping (agent (value ^$var4)) ; the case roles (theme (value ^$var2))) (^$var1 (null-sem +)) ; blocks compositional analysis of preps (^$var3 (null-sem +)))) )

(cancer (cancer-n1 (cat n) (anno (def "a disease") (ex "") (comments "") ) (syn-struc ((n ((root $var1) (cat n) (opt +))) ; animal part as modifier (root $var0) (cat n) ; cancer )) (sem-struc (CANCER (location (value ^$var1) (sem animal-part))) )

(cancer-n2 (cat n) (anno (def "a sign of the zodiac") (ex "") (comments "") ) (syn-struc ((root $var0) (cat n) )) (sem-struc (CANCER-ZODIAC) )

Currently Available Static Knowledge Sources for English: Ontology of about 6,500 concepts (about 95,000 property-value pairs) English lexicon of about 40,000 entries Fact repository of about 20,000 facts (outside medical domain) English Onomasticon of about 350,000 entries Tokenization knowledge, morphological and syntactic grammars for a number of languages

Preprocessor Input Text Syntactic Analyzer Grammar: Ecology Morphology Syntax Lexicon and Onomasticon Static Knowledge Resources Semantic Analyzer Ontology and Fact Repository TMR Processing Modules The analyzer’s conceptual architecture (in reality, not strictly pipelined)

The basic (“who did what to whom”) semantic dependency is derived, in the general case, on the basis of a)lexical-semantic expectations (selectional restrictions) recorded in the ontology and the lexicon and b)syntactic dependency derived from the results of syntactic analysis.

The beginnings of system evaluation Run I: “raw” Run II: preprocessor output correct; Run III: preprocessor and syntactic analysis output correct

In addition to the basic semantic dependency, TMRs also include parameterized information provided by the microtheories of aspect, modality (including speaker attitudes), time, style and others. Most of these microtheories have been implemented. All would benefit from further work. We are also actively looking into possibilities of borrowing some microtheories -- either in toto or partially.

FrameNet: Another example of semantic representation Frame Semantics (Fillmore 1976, 1977,..) Frame: a conceptual structure or prototypical situation Frame elements (roles) – Identify participants of the situation – Are local to their frame Frame evoking elements (verbs, nouns, adjectives) introduce frames E.g. VERDICT: [The jury] Judge convicted [him] Defentant [on the counts of theft] Charges. On Thursday [a jury] Judge found [the youth] Defendant [guilty of wounding Mr Lay] Finding Berkeley FrameNet Project Database of frames for core lexicon of English Current release: 610 frames, about 9000 lexical units

Types of Relations FrameNet Relations Frame hierarchy: inherits Subframes Contextual Relations between instantiated frames and roles Syntactic and/or semantic embedding Discourse relations Anaphoric relations Inferences On the basis of both

A Case Study In the first trial in the world in connection with the terrorist attacks of 11 September 2001, the Higher Regional Court of Hamburg has passed down the maximum sentence. Mounir al Motassadeq will spend 15 years in prison. The 28-year-old Moroccan was found guilty as an accessory to murder in more than 3000 cases.

FrameNet „as a Net“ – Frame-to-Frame Relations – Subframe relation Super frame represents complex event Subframes represent sub-events Subframes usually inherit some roles of the super frame Criminal process ArraignmentArrestSentencingTrial Charge Judge Defendant Defense Court Jury Offense Prosecution Charge Defendant...

Local Roles In the first trial in the world in connection with [the [terrorist] Assailant attacks of [11 September 2001] Time ] Case, [the Higher Regional Court of Hamburg] Court has passed down the [maximum] Type sentence.

Local Roles [Mounir al Motassadeq] Inmates will spend [15 years] Duration in prison.

Local Roles [The 28-year-old Moroccan] Defendant was found [guilty] Finding as [an accessory to [murder] FocalEntity [in more than 3000 cases] Victim ] Charge.

Unfilled Roles TargetFrameFrame rolesFiller (given vs. Induced) trial TRIAL CASE terrorist attacks(1) CHARGE accessory to murder(2) COURT Higher Regional Court (3) DEFENDANT...28-year-old Moroccan(4) attacks ATTACK ASSAILANT terrorist(5) VICTIM...(6) TIME (exth.)11 September 2001(7) sentence SENTENCING CONVICT Mounir al Motassadeq(8)COURT Higher Regional Court(9)TYPE...maximum sentence (10) prison PRISON INMATES...Mounir al Motassadeq(11) DURATION (exth.) 15 years(12) found VERDICT CASE terrorist attacks(13) CHARGE accessory to murder(14)DEFENDANT 28- year-old Moroccan(15)FINDING...guilty(16) accessoryASSISTANCE CO-AGENT (17) FOCAL_ENTITY murder(18) HELPER...28-year-old Moroccan(19) murder KILLING KILLER (20) VICTIM...m.t cases(21)

TargetFrameFrame rolesFiller (given vs. Induced) trial TRIAL CASE terrorist attacks (1) CHARGE accessory to murder(2) COURT Higher Regional Court (3) DEFENDANT...28-year-old Moroccan(4) attacks ATTACK ASSAILANT terrorist (5) VICTIM...(6) TIME (exth.)11 September 2001(7) sentence SENTENCING CONVICTMounir al Motassadeq(8) COURT Higher Regional Court(9) TYPE...maximum sentence(10) prison PRISON INMATES...Mounir al Motassadeq(11) DURATION (exth.) 15 years(12) Found VERDICT CASE terrorist attacks(13) CHARGE accessory to murder(14) DEFENDANT 28-year-old Moroccan(15) FINDING...guilty(16) accessoryASSISTANCE CO-AGENT (17) FOCAL_ENTITY murder(18) HELPER...28-year-old Moroccan(19) murder KILLING KILLER (20) VICTIM...m.t cases(21)

TargetFrameFrame rolesFiller (given vs. Induced) trial TRIAL CASE terrorist attacks(1) CHARGE accessory to murder(2) COURT Higher Regional Court (3) DEFENDANT...28-year-old Moroccan(4) attacks ATTACK ASSAILANT terrorist(5) VICTIM...(6) TIME (exth.)11 September 2001(7) sentence SENTENCING CONVICT Mounir al Motassadeq(8) COURT Higher Regional Court(9) TYPE...maximum sentence(10) prison PRISON INMATES...Mounir al Motassadeq(11) DURATION (exth.) 15 years(12) found VERDICT CASE terrorist attacks(13) CHARGE accessory to murder(14) DEFENDANT 28-year-old Moroccan(15) FINDING...guilty(16) accessoryASSISTANCE CO-AGENT (17) FOCAL_ENTITY murder(18) HELPER...28-year-old Moroccan(19) murder KILLING KILLER (20) VICTIM...m.t cases(21)

Linking Frames and Roles in Context At the instance level given frame instances f 1 :F 1 and f 2 :F 2, where – f 1 and f 2 stand in a contextual relation (syn, sem, discourse) – frame types F 1 and F 2 stand in some frame relation => identify role instances (referents) of f 1 and f 2 (r 1 (= r 0 ) = r 2 ) frame relation context-related instances inferred relation

Linking Frames and Roles in Context In the first trial in the world in connection with the terrorist attacks of 11 September 2001, the Higher Regional Court of Hamburg has passed down the maximum sentence. Criminal Process Trial Sentencing Court frame relation

Linking Frames and Roles in Context In the first trial (f 1 ) in the world in connection with the terrorist attacks of 11 September 2001, [the Higher Regional Court of Hamburg] (r 2 ) has passed down the maximum sentence (f 2 ). The Higher Regional Court of Hamburg Functional Embedding Criminal Process Trial Sentencing Court frame relation context-related instances

Linking Frames and Roles in Context The Higher Regional Court of Hamburg Functional Embedding Criminal Process Trial Sentencing Court frame relationcontext-related instancesinferred relation In the first trial (f 1 ) in the world in connection with the terrorist attacks of 11 September 2001, [the Higher Regional Court of Hamburg] (r 2 =r 0 = r 1 ) has passed down the maximum sentence (f 2 ).

Linking Frames and Roles in Context At the type level (more involved) If instances of frame roles f 1 :F 1 and f 2 :F 2 are often found co- referent within particular contextual relations => Hypothesize a frame relation between F 1 and F 2 (no) frame relation context-related instances inferred relation

Linking Frames and Roles in Context (no) frame relation context-related instances inferred relations … the Higher Regional Court of Hamburg has passed down the Maximum sentence. [Mounir al Motassadeq] will spend 15 years in prison. Sentencing Prison Convict Inmates Discourse Relation New Frame Relation (Role Binding: Convict=Inmates) (Co-reference)

CRIMINAL PROCESS SENTENCING (1)TRIAL (1) VERDICT (3) Defendant KILLING (3) Inferred Relation Contextual Relation Killer Subframe/FE PRISON (2) InmatesDuration ASSISTANCE (3) HelperCo_agentFocal_entityVictim ConvictType Court CaseCharge CaseCharge Court Finding (1)sentence number Frame, Contextual, and Inferred Relations

CRIMINAL PROCESS SENTENCINGTRIAL VERDICT Defendant (the Moroccan) KILLING Inference Contextual Relations Killer Hierarchy/Subframe/FE PRISON Inmates (Motus.) Duration (15Y) ASSISTANCE HelperCo_agentGoal (murder) Victim (3000) ConvictDuration (maximum) Court (Hmbg.) Case (9/11) Charge CaseCharge (accessory) In the first trial.. the higher Regional Court.. has passed down the maximum sentence. Mounir al Motussadeq will spend 15 years in prison. The 28-year-old Moroccan was found guilty as an accessory to murder in cases.

Statistical Semantic Role Labeling

References Jurafsky, D. & J. H. Martin, Speech and Language Processing, Prentice-Hall, (Chapters 9 and 10) Helmreich, S., From Syntax to Semantics, Presentation in the Course, November Nirenburg, S. & V. Raskin, Ontological Semantics, MIT Press, Wordnet, Suggested Upper Merged Ontology (SUMO),