C.A.R.L.A. Computer-Assisted Related Language Adaptation

C.A.R.L.A.

Computer-Assisted Related Language

Adaptation


AdaptationWhat is the distinction between a dialect and a related language?



• It lies in the socio-political viewpoint.

We call Chinese a language as well as Spanish, French, and Portuguese, but the dialects of Chinese are as diverse as Spanish, French, and Portuguese.



• It lies in the socio-political viewpoint.

We call Chinese a language as well as Spanish, French, and Portuguese, but the dialects of Chinese are as diverse as Spanish, French, and Portuguese.

• “The difference between a dialect and a language is an Army and a Navy” (reputedly said by Bernard Bloch).


AdaptationWhat is related language adaptation?



• Produce a draft of a text in one dialect/language by making changes in a source text.




• Manual Adaptation has succeeded (Huambisa adapted by hand from Aguaruna in Peru & published in 1975).




• Manual Adaptation has succeeded (Huambisa adapted by hand from Aguaruna in Peru & published in 1975).

• Manual Adaptation is tedious, time-consuming, and prone to human error.


AdaptationWhat is computer-assisted related language adaptation?



• It automates the systematic changes.




• It avoids human error.




• It avoids human error.

• The results are not a perfect translation: expect them to need to be revised, checked, and polished just like any other draft.

The Linguistic Basis

• Early machine-translation efforts failed.



• Machine-translation succeeds when limited to texts of certain kinds (simple declaratives and imperatives, e.g.).



• Machine-translation succeeds when limited to texts of certain kinds (simple declaratives and imperatives, e.g.).

So why does C.A.R.L.A. work on all kinds of texts?



• If two languages are related, then the differences between them are due to changes that occurred over time.




• Related languages tend to change in a systematic fashion.





• The languages still share a common “conceptual framework”: the role that a particular form plays in the syntax and/or semantics usually stays the same.





• The languages still share a common “conceptual framework”: the role that a particular form plays in the syntax and/or semantics usually stays the same.

• When one can encode most of the systematic changes in a computational tool, then the tool can make these changes successfully.


What are some of the types of systematic changes?



• Phonological:

Jesús María Cora to Presidio de los Reyes Cora (Mexico)teyan teyenseyan seyenayan ayenayaa ayeeajta ajtatatzari tatzaricatu catu



• Phonological:

Jesús María Cora to Presidio de los Reyes Cora (Mexico)teyan teyenseyan seyenayan ayenayaa ayeeajta ajtatatzari tatzaricatu catu

Regular sound change: a e / y _



• Morphological:

Axininca Campa to Caquinte (Peru)-aa -ha ‘liquid’-aa -aha ‘repetitive’-aa -a ‘non-future, reflexive’-aa -a ‘non-future’



• Morphological:

Huallaga Quechua to North Junin Quechua (Peru)-ra -rqa ‘simple past’-ra -ra: ‘exhibit (verbalizer)’-ra -ra: ‘state durative’-ra -ri ‘punctiliar’



• Syntactic:

In Huallaga Quechua, phasal verbs take infinitive objects.

In other dialects, these verbs take adverbial complements.



• Syntactic:

In Bantu languages, every noun is a member of one of up to about 20 classes. Head nouns and their modifiers must agree within a noun phrase.

If a noun changes its class between a source and target dialect, then every modifier in the noun phrase must reflect this change in class agreement.



• Semantic/pragmatic:

Imperatives with an overt 2nd person pronoun:

Chimborazo Quichua (Ecuador) normally includes the pronoun.

In Imbabura Quichua, this is grammatical, but it carries with it an abrupt, bossy tone.

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What are some possible approaches?

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• Re-write rules (change an instance of ‘this’ to ‘that’).

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• It is oriented to the surface-level.

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• It is useful when the languages involved are very closely related and when the nature of the changes between them is predominantly regular sound change and/or orthography change.

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• It has been used successfully in places like Peru, Mexico, and Togo.

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• It has been used successfully in places like Peru, Mexico, and Togo.

• Programs include: CC and WRDCHG/WrdOChg.

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• Partial morphological analysis.

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• It has a limited capability to do automated morphological parsing.

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• It is useful when the languages involved are very closely related and when the morphology is very simple.

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• It has been used successfully in places like Ghana and Burkina Faso.

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• It has been used successfully in places like Ghana and Burkina Faso.

• Programs include: IT and Shoebox for Windows.

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• Full morphological analysis.

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• It can do fully automated morphological parsing and then map the abstract morphological representations from one language to another.

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• It is useful when the languages involved have non-simple morphologies.

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• It can be successful between mutually unintelligible, but related, languages.

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• It can be successful between mutually unintelligible, but related, languages.

• It has been used successfully in many parts of the world.

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• It also has several side benefits:

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• It produces interlinearized, glossed text.

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• It acts as a spelling checker.

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• It acts as a morphological model checker.

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• It acts as a morphological model checker.

•Programs include the AMPLE/SenTrans/STAMP suite.

C.A.R.L.A.What are the major steps used in the full morphological approach?


Source Language

Orthographic Form

Target Language


Source Language

Orthographic Form TextIn

Target Language


Source Language


Normalized Form

Target Language


Source Language


Normalized Form Analysis

Target Language


Source Language



Analyzed Form

Target Language


Source Language



Analyzed Form Transfer

Target Language


Source Language




Rearranged Analyzed Form

Target Language


Source Language




Rearranged Analyzed Form Synthesis

Target Language


Source Language





Normalized Form

Target Language


Source Language





Normalized Form TextOut

Target Language


Source Language





Normalized Form TextOut

Orthographic Form

Target Language

C.A.R.L.A.What are some examples of the full morphological approach?


Huallaga Quechua allchakaachimanga

Huamalies Quechua


Huallaga Quechua allchakaachimanga allchaka:chimanqa (TextIn)

Huamalies Quechua


Huallaga Quechua allchakaachimanga allchaka:chimanqa (TextIn) allcha-ka:-chi-ma-nqa (Analysis)*allcha PASS CAUS 1O 3FUT

Huamalies Quechua


Huallaga Quechua allchakaachimanga allchaka:chimanqa (TextIn) allcha-ka:-chi-ma-nqa (Analysis)*allcha PASS CAUS 1O 3FUT *katra PASS CAUS 1O 3FUT (Transfer)

Huamalies Quechua


Huallaga Quechua allchakaachimanga allchaka:chimanqa (TextIn) allcha-ka:-chi-ma-nqa (Analysis)*allcha PASS CAUS 1O 3FUT *katra PASS CAUS 1O 3FUT (Transfer) kacha -ka: -si -ma -nqa (Synthesis) kachaka:simanqa

Huamalies Quechua


Huallaga Quechua allchakaachimanga allchaka:chimanqa (TextIn) allcha-ka:-chi-ma-nqa (Analysis)*allcha PASS CAUS 1O 3FUT *katra PASS CAUS 1O 3FUT (Transfer) kacha -ka: -si -ma -nqa (Synthesis) kachaka:simanqa kachakaasimanga (TextOut)

Huamalies Quechua


Pachitea Quechua Aywarkaykargan

Huanca Quechua


Pachitea Quechua Aywarkaykargan aywarkaykarqan (TextIn)

Huanca Quechua


Pachitea Quechua Aywarkaykargan aywarkaykarqan (TextIn) aywa-rka-yka-rqa-n (Analysis)*aywa PLIMPF IMPF PST 3

Huanca Quechua


Pachitea Quechua Aywarkaykargan aywarkaykarqan (TextIn) aywa-rka-yka-rqa-n (Analysis)*aywa PLIMPF IMPF PST 3 *ri (Transfer - 1)

Huanca Quechua


Pachitea Quechua Aywarkaykargan aywarkaykarqan (TextIn) aywa-rka-yka-rqa-n (Analysis)*aywa PLIMPF IMPF PST 3 X *ri IMPF PLIMPF PST 3 (Transfer - 2)

Huanca Quechua


Pachitea Quechua Aywarkaykargan aywarkaykarqan (TextIn) aywa-rka-yka-rqa-n (Analysis)*aywa PLIMPF IMPF PST 3 X *ri IMPF PLIMPF PST 3 (Transfer)

Huanca Quechua


Pachitea Quechua Aywarkaykargan aywarkaykarqan (TextIn) aywa-rka-yka-rqa-n (Analysis)*aywa PLIMPF IMPF PST 3 X *ri IMPF PLIMPF PST 3 (Transfer) li -ya: -lka -la -(Synthesis) liyalkala

Huanca Quechua


Pachitea Quechua Aywarkaykargan aywarkaykarqan (TextIn) aywa-rka-yka-rqa-n (Analysis)*aywa PLIMPF IMPF PST 3

X *ri IMPF PLIMPF PST 3 (Transfer) li -ya: -lka -la -(Synthesis) liyalkala Liyalkala (TextOut)

Huanca Quechua


Machiguengaokenkiagani

Caquinte


MachiguengaokenkiaganiokeNkiagani (TextIn)

Caquinte


MachiguengaokenkiaganiokeNkiagani (TextIn)o-keNki-agani (Analysis)3F tell.about PASS

Caquinte



tell DAT (Transfer - 1)

Caquinte


MachiguengaokenkiaganiokeNkiagani (TextIn)o-keNki-agani (Analysis)3F tell.about PASS 3M tell DAT PAS 3FO (Transfer - 2)

Caquinte



3M tell DAT PAS NF 3FO (Transfer - 3)

Caquinte


MachiguengaokenkiaganiokeNkiagani (TextIn)o-keNki-agani (Analysis)3F tell.about PASS 3M tell DAT PAS NF 3FO (Transfer)

Caquinte



i- tsave -ako -hi -i -ro (Synthesis) itsavetakohitiro

Caquinte



i- tsave -ako -hi -i -ro (Synthesis) itsavetakohitiro itsavetacojitoro (TextOut)

Caquinte

C.A.R.L.A.


Adaptation

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C.A.R.L.A. Computer-Assisted Related Language Adaptation