traduzione automatica da italiano a lingua italiana dei...
TRANSCRIPT
1 Interaction
Models Group
Il progetto ATLAStraduzione automatica da Italiano a Lingua Italiana dei Segni (LIS)
Alessandro MazzeiDip. Informatica
Università di Torino
21-12-2010
2 Interaction
Models Group
Machine Translation
The spirit is willing,
but the flesh is weakВодка сильна,
но мясо гнило
3 Interaction
Models Group
Semantic Structure
SyntacticStructure
Words
Semantic Structure
SyntacticStructure
Words
Source Sentence Target Sentence
Triangolo di Vauquois Interlingua
Analisi Generazione
4 Interaction
Models Group
Semantic Structure
SyntacticStructure
Words
Semantic Structure
SyntacticStructure
Words
Source Sentence Target Sentence
Triangolo di Vauquois Interlingua
Analisi Generazione
Direct
Transfer
5 Interaction
Models Group
Semantic Structure
SyntacticStructure
Words
Semantic Structure
SyntacticStructure
Words
Source Sentence Target Sentence
The KANT Project
Triangolo di Vauquois Interlingua
Analisi Generazione
Direct
Transfer
6 Interaction
Models Group
Il progetto ATLAS
Traduzione automatica da Italiano a Lingua Italiana dei Segni
7 Interaction
Models Group
La Lingua Italiana dei segni
La LIS è “segnata” da migliaia di persone
È una lingua a tutti gli effetti: lessico, morfologia, sintassi
Ha anche elementi di iconicità
Spesso è l'unica lingua conosciuta dai sordi
Paesi diversi, lingue dei segni diverse
8 Interaction
Models Group
Linguistica della LIS
Diversi articolatori: parallelismo
Organizzazione spaziale della frase
No preposizioni, genere, articoli
Plurale
SOV
Dialetti regionali
Ancora pochi studi linguistici
Non ha una forma scritta (!!!)
Meteo19-3
9 Interaction
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Semantic Structure
SyntacticStructure
Words
Semantic Structure
SyntacticStructure
Words
Italian Sentence LIS Sentence
Triangolo di Vauquois Interlingua
Analysis Generation
Direct
10 Interaction
Models Group
Outline
Rule-Based Translation Architecture in ATLAS
Analisi dell'Italiano
– Turin University Parser (TUP)
– Turin University Semantic Interprete (TUSI)
Generazione in LIS
Conclusioni
11 Interaction
Models Group
Outline
Rule-Based Translation Architecture in ATLAS
Analisi dell'Italiano
– Turin University Parser (TUP)
– Turin University Semantic Interprete (TUSI)
Generazione in LIS
Conclusioni
12 Interaction
Models Group
Rule-Based Translation Architecture
OpenCCGTUrinLinguisticEnviroment
ITALIANO
Logica dei Predicati
Analisi Generazione
GLOSSE+Feats(AEW-LIS)
13 Interaction
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Architecture details
14 Interaction
Models Group
Outline
Rule-Based Translation Architecture in ATLAS
Analisi dell'Italiano
– Turin University Parser (TUP)
– Turin University Semantic Interprete (TUSI)
Generazione in LIS
Conclusioni
15 Interaction
Models Group
Turin University Parser
A wide-coverage rule-based dependency parser
Rule-Based: chunking + coord + verb-sub-cat
Morpho-syntatctic dependency annotation
16 Interaction
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Dependency-chunking...(ART def BEFORE (NOUN (TYPE COMMON) ) DET+DEF-ARG )
...
... le nuvole ...nuvole
ledet-def-arg
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Models Group
VerbSubCat taxonomyVERBS
TRANS INTRANS
...aumenterannotrans-dir-disc-indobj ... intrans-indobj-ssubj
...
...
... ...
......
18 Interaction
Models Group
Turin University Parser
Domani aumenteranno
2
1
Domani le nuvole aumenteranno
nuvole
ledet-noun
nuvole
le
aumenterannoverb-sbj
det-def-argdomani
advb-rmod-time
19 Interaction
Models Group
Anatomy of the TUP
I. Grammar: CFG, TAG, Dependency grammar …
II. Algorithm:
i. Search strategy: top-down, bottom-up, L2R, …
ii. Memory organization: depth-first, back-tracking,
dynamic programming, all-paths, ...
III.Oracle: Probabilistic, heuristic, ...
20 Interaction
Models Group
Domani le nuvole sono in aumento al nord
21 Interaction
Models Group
Outline
Rule-Based Translation Architecture in ATLAS
Analisi dell'Italiano
– Turin University Parser (TUP)
– Turin University Semantic Interprete (TUSI)
Generazione in LIS
Conclusioni
22 Interaction
Models Group
DepTree ⇝ Logica dei Predicati
First Order Formulas
Predicate-Argument relations
Semantic Roles
TUSI Ontologies
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Ontologies: world + domain
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Ontologies: language
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Computing Meaning by Recursion
26 Interaction
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Ontological Connection Paths
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Ontological Path ⇝ Logica dei Predicati
Per domani quindi nubi in aumento al nord ...
[meteo 19-F4]
event(E=cloud-increase') ⋀
AGENT(E,cloud') ⋀
LOC(E,north') ⋀
TIME(E,tomorrow')
⇝
28 Interaction
Models Group
Outline
Rule-Based Translation Architecture in ATLAS
Analisi dell'Italiano
– Turin University Parser (TUP)
– Turin University Semantic Interprete (TUSI)
Generazione in LIS
Conclusioni
29 Interaction
Models Group
Generazione
Logica dei Predicati
GLOSSE+Feats
(AEW-LIS)
OpenCCGLIS-CCG
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OpenCCG
Combinatory Categorial Grammar
Bidirectional: speed-up in grammar development
Syntax / Semantics: Features Unification with Hybrid Logic
Chart – Agenda
Statistical pruning: symbolic-statistical chart
31 Interaction
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LIS-CCG
LEX SynCAT SemCAT
nuvolaU
NP[position=U X] X=cloud'
domaniN
S[position=N E]
/ S[position=N E] TIME(E,tomorrow')
nordU
S[position=U E]
/ S[position=U E] LOC(E,north')
nuvola-aumentare
US
[E]\ NP
[position=U Y]
event(E=cloud-increase') ⋀
AGENT(E,Y)
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CCG derivation
S : event(E=cloud-increase')⋀AGENT(E,cloud')⋀LOC(E,north')⋀TIME(E,tomorrow')
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CCG derivation
domaniN
S :
SE / S
E
>S :
TIME(E,tomorrow')
event(E=cloud-increase')⋀AGENT(E,cloud')⋀LOC(E,north')⋀TIME(E,tomorrow')
event(E=cloud-increase')⋀AGENT(E,cloud')⋀LOC(E,north')
34 Interaction
Models Group
CCG derivation
SE / S
E
>S :
S :
SE / S
E
>S :
LOC(E,north')TIME(E,tomorrow')
event(E=cloud-increase')⋀AGENT(E,cloud')
event(E=cloud-increase')⋀AGENT(E,cloud')⋀LOC(E,north')⋀TIME(E,tomorrow')
event(E=cloud-increase')⋀AGENT(E,cloud')⋀LOC(E,north')
domaniN nord
U
35 Interaction
Models Group
CCG derivation
SE / S
ENP
XS
E \ NP
Y
>
<S :
S :
SE / S
E
>S :
event(E=cloud-increase')⋀AGENT(E,Y)LOC(E,north') X=cloud'TIME(E,tomorrow')
domaniN nord
U nuvola
U nuvola-aumentare
U
event(E=cloud-increase')⋀AGENT(E,cloud')
event(E=cloud-increase')⋀AGENT(E,cloud')⋀LOC(E,north')⋀TIME(E,tomorrow')
event(E=cloud-increase')⋀AGENT(E,cloud')⋀LOC(E,north')
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Proprietà Glosse+Feats
domaniN nord
U nuvola
U nuvola-aumentare
U
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Glosse+Feats: posizione
domaniN nord
U nuvola
U nuvola-aumentare
U
Neutral = N
Up = U Posizione nello Spazio
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Models Group
Glosse+Feats: ordine segni
domaniN nord
U nuvola
U nuvola-aumentare
U
L'ordine dei segni nella frase è S(O)V
39 Interaction
Models Group
Glosse+Feats: verbo-argomenti
domaniN nord
U nuvola
U nuvola-aumentare
U
Soggetto e Verbo sono segnati nella stessa posizione
40 Interaction
Models Group
Glosse+Feats: pragmatica
Le nuvole vengono segnate al “nord”: Up
domaniN nord
U nuvola
U nuvola-aumentare
U
41 Interaction
Models Group
Sintesi in LIS
domaniN nord
U nuvola
U nuvola-aumentare
U
42 Interaction
Models Group
Outline
Rule-Based Translation Architecture in ATLAS
Analisi dell'Italiano
– Turin University Parser (TUP)
– Turin University Semantic Interprete (TUSI)
Generazione in LIS
Conclusioni
43 Interaction
Models Group
Conclusioni
Traduzione interlingua = analisi profonde
Basi di conoscenza
– Grammatiche: Italiano (Dipendenze), LIS (CCG)
– Ontologie: mondo e lingua, meteo
– Semantic Interpretation
Work in Progress:
– Topic/Focus
– Discorso (anafore, etc.)
44 Interaction
Models Group
Considerazioni “politiche” sulla ricerca
Quanta ricerca di base c'è in ATLAS?
L'applicazione è una conseguenza, non un obiettivo
Il "sapere del fare" non esiste, esiste la conoscenza
Dovere dell'Università è farlo capire a tutti
45Time flies like an arrowInteraction
Models Group
Grazie per l'attenzione :)
ATLAS project is co-funded by Regione Piemonte within the “Converging Technologies - CIPE 2007” framework (Research Sector: Cognitive Science and ICT).
46 Interaction
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47 Interaction
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Evoluzione della MT
1930: Troyanskii's “translating machines”
1947: Guerra fredda
1956: Dartmouth Seminar
1966: ALPAC report
1990: La rinascita
2000: On-line MT
2002: Statistical Phrase MT
48 Interaction
Models Group
Logica XML ⇡ Generazione
domani_N nord_U nuvola_U nuvola-aumentare_U...
<?xml version="1.0" encoding="UTF-8"?><xml> <lf> <satop nom="c1:meteo-status-situation"> <prop name="cloud-increase"/> <diamond mode="AGENT"> <nom name="c2:clouds"/> <prop name="cloud"/> </diamond> <diamond mode="LOCATION"> <nom name="n1:it-northern-region"/> <prop name="north"/> </diamond> <diamond mode="TIME"> <nom name="t1:deictic-day-description"/> <prop name="tomorrow"/> </diamond> </satop> </lf></xml>
OpenCCG LIS-CCG
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Logica dei Predicati ⇡ Logica XML<?xml version="1.0" encoding="UTF-8"?><xml> <lf> <satop nom="c1:meteo-status-situation"> <prop name="cloud-increase"/> <diamond mode="AGENT"> <nom name="c2:clouds"/> <prop name="cloud"/> </diamond> <diamond mode="LOCATION"> <nom name="n1:it-northern-region"/> <prop name="north"/> </diamond> <diamond mode="TIME"> <nom name="t1:deictic-day-description"/> <prop name="tomorrow"/> </diamond> </satop> </lf></xml>
∃ (ev1 t
1 l
1 x
1) (cloud-increase(ev
1) ⋀
time(ev1, t
1) ⋀ deictic-descr(t
1, t
2) ⋀
id(t2,tomorrow) ⋀ location(ev
1, l
1) ⋀
id(l1, nord) ⋀ agent(ev
1, x
1) ⋀ cloud(x
1))⇡
50 Interaction
Models Group
Time flies like an arrow. Fruit flies like a banana.
Every time I fire a linguist, the performance of our speech recognition system goes up. (Fred Jelinek)
51 Interaction
Models Group
Ontological Path ⇝ Logica dei Predicati
Per domani quindi nubi in aumento al nord ...
[meteo 19-F4]
∃ (ev1 t
1 l
1 x
1) (cloud-increase(ev
1) ⋀
time(ev1, t
1) ⋀ deictic-descr(t
1, t
2) ⋀
id(t2,tomorrow) ⋀ location(ev
1, l
1) ⋀
id(l1, nord) ⋀ agent(ev
1, x
1) ⋀ cloud(x
1))
⇝
52 Interaction
Models Group
CCG derivation
domaniN nord
U nuvola
U nuvola-aumentare
U
S / S NP S \ NP
>
<S
S
S / S
>S
53 Interaction
Models Group
LIS-CCG
DOMANI_N NUVOLA_U NUVOLA-AUMENTARE_U
S/S NP S\NP
>
<S
S
∃ (ev1 t
1 x
1) (cloud-increase(ev
1) ⋀ time(ev
1, t
1) ⋀ deictic-descr(t
1, t
2)
⋀ id(t2,tomorrow) ⋀ agent(ev
1, x
1) ⋀ cloud(x
1))
54 Interaction
Models Group
LIS-CCG
DOMANI_N NUVOLA_U NUVOLA-AUMENTARE_U
S/S NP S\NP
>
<S
S
∃ (ev1 t
1 x
1) (cloud-increase(ev
1) ⋀ time(ev
1, t
1) ⋀ deictic-descr(t
1, t
2)
⋀ id(t2,tomorrow) ⋀ agent(ev
1, x
1) ⋀ cloud(x
1))
55 Interaction
Models Group
LIS-CCG
DOMANI_N NUVOLA_U NUVOLA-AUMENTARE_U
S/S NP S\NP
>
<S
S
∃ (ev1 t
1 x
1) (cloud-increase(ev
1) ⋀ time(ev
1, t
1) ⋀ deictic-descr(t
1, t
2)
⋀ id(t2,tomorrow) ⋀ agent(ev
1, x
1) ⋀ cloud(x
1))
56 Interaction
Models Group
LIS-CCG
DOMANI_N NUVOLA_U NUVOLA-AUMENTARE_U
S/S NP S\NP
>
<S
S
∃ (ev1 t
1 x
1) (cloud-increase(ev
1) ⋀ time(ev
1, t
1) ⋀ deictic-descr(t
1, t
2)
⋀ id(t2,tomorrow) ⋀ agent(ev
1, x
1) ⋀ cloud(x
1))
57 Interaction
Models Group
LIS-CCG
DOMANI_N NUVOLA_U NUVOLA-AUMENTARE_U
S/S NP S\NP
>
<S
S
∃ (ev1 t
1 x
1) (cloud-increase(ev
1) ⋀ time(ev
1, t
1) ⋀ deictic-descr(t
1, t
2)
⋀ id(t2,tomorrow) ⋀ agent(ev
1, x
1) ⋀ cloud(x
1))
58 Interaction
Models Group
Italiano ⇝ Logica dei PredicatiPer domani quindi nubi in aumento al nord, con precipitazioni a carattere sparso anche temporalesche, tendenza poi a schiarite dalla serata.
[meteo 19-F4]
I. ∃ (ev1 t
1 l
1 x
1) (cloud-increase(ev
1) ⋀
time(ev1, t
1) ⋀ deictic-descr(t
1, t
2) ⋀
id(t2,tomorrow) ⋀ location(ev
1, l
1) ⋀
id(l1, nord) ⋀ agent(ev
1, x
1) ⋀ cloud(x
1))
II. ∃ (ev2) (rain(ev
2) ⋀
has-event-width(ev2,local-phenomenon))
⋀ ∃ (ev3) (storm(ev
3))
III. ∃ (ev4,x
4, t
4, d
4) (cloud-decrease(ev
4) ⋀
agent(ev4, x
4) ⋀ cloud(x
4) ⋀ time(ev
4, t
4) ⋀
time-ref(t4, e
4) ⋀ afternoon(d
4))
⇝
59 Interaction
Models Group
Italiano ⇝ Logica dei PredicatiPer domani quindi nubi in aumento al nord, con precipitazioni a carattere sparso anche temporalesche, tendenza poi a schiarite dalla serata.
[meteo 19-F4]
I. ∃ (ev1 t
1 l
1 x
1) (cloud-increase(ev
1) ⋀
time(ev1, t
1) ⋀ deictic-descr(t
1, t
2) ⋀
id(t2,tomorrow) ⋀ location(ev
1, l
1) ⋀
id(l1, nord) ⋀ agent(ev
1, x
1) ⋀ cloud(x
1))
II. ∃ (ev2) (rain(ev
2) ⋀
has-event-width(ev2,local-phenomenon))
⋀ ∃ (ev3) (storm(ev
3))
III. ∃ (ev4,x
4, t
4, d
4) (cloud-decrease(ev
4) ⋀
agent(ev4, x
4) ⋀ cloud(x
4) ⋀ time(ev
4, t
4) ⋀
time-ref(t4, e
4) ⋀ afternoon(d
4))
⇝
60 Interaction
Models Group
LIS-CCG
LEX SynCAT SemCAT
nuvolaU
NP[position=U X] X=cloud'
domaniN
S[position=N E]
/ S[position=N E] E <TIME> tomorrow'
nordU
S[position=U E]
/ S[position=U E] E <LOC> north'
nuvola-aumentare
US
[E]\ NP
[position=U Y] E=cloud-increase'<AGENT>Y
61 Interaction
Models Group
CCG derivation
S E=cloud-increase'(<AGENT>cloud' <LOC>north' <TIME>tomorrow')
62 Interaction
Models Group
CCG derivation
domaniN
S
S / S
>S
E<TIME>tomorrow'
E=cloud-increase'(<AGENT>cloud' <LOC>north' <TIME>tomorrow')
E=cloud-increase'(<AGENT>cloud' <LOC>north')
63 Interaction
Models Group
CCG derivation
domaniN nord
U
S / S
>S
S
S / S
>S
E<LOC>north'E<TIME>tomorrow'
E=cloud-increase'(<AGENT>cloud')
E=cloud-increase'(<AGENT>cloud' <LOC>north')
E=cloud-increase'(<AGENT>cloud' <LOC>north' <TIME>tomorrow')
64 Interaction
Models Group
CCG derivation
domaniN nord
U nuvola
U nuvola-aumentare
U
S / S NP S \ NP
>
<S
S
S / S
>S
E=cloud-increase'<AGENT> YE<LOC>north' cloud'E<TIME>tomorrow'
E=cloud-increase'(<AGENT>cloud')
E=cloud-increase'(<AGENT>cloud' <LOC>north')
E=cloud-increase'(<AGENT>cloud' <LOC>north' <TIME>tomorrow')
65 Interaction
Models Group
Ontological Path ⇝ Logica dei Predicati
Per domani quindi nubi in aumento al nord ...
[meteo 19-F4]
∃ (ev1 t
1 l
1 x
1) (cloud-increase(ev
1) ⋀
time(ev1, t
1) ⋀ deictic-descr(t
1, t
2) ⋀
id(t2,tomorrow') ⋀ location(ev
1, l
1) ⋀
id(l1, nord') ⋀ agent(ev
1, x
1) ⋀ cloud(x
1))⇝
Ecloud-increase<AGENT> cloud' ⋀
Ecloud-increase<LOC> north' ⋀
Ecloud-increase<TIME> tomorrow'
66 Interaction
Models Group
1. Dependency-chunking...(ADJ-QUALIF BEFORE (ADV (TYPE MANNER) ) ADVMOD-MANNER )
...
... davvero veloce ...davvero
veloceADVMOD-MANNER
nuvole
le
aumenterannosbj-verb
det-noundomani
mod-adv
67 Interaction
Models Group
1. Dependency-chunking...(ART def BEFORE (NOUN (TYPE COMMON) ) DET+DEF-ARG )
...
... le nuvole ...
nuvole
le
aumenterannosbj-verb
det-noundomani
mod-adv