Breaking the Language Barrier: A Game-Changing Approach

8/12/2019 Breaking the Language Barrier: A Game-Changing Approach

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Breaking the Language Barrier:

A Game-Changing Approach

L1

L2

Version 0.25

Ziyuan [email protected]

https://sites.google.com/site/yaoziyuan/

24 June 2012

his !or" is license# un#er a $reati%e $ommons &ttri'ution (.0 )nporte# *icense.
mailto:[email protected]://sites.google.com/site/yaoziyuan/http://creativecommons.org/licenses/by/3.0/http://creativecommons.org/licenses/by/3.0/https://sites.google.com/site/yaoziyuan/http://creativecommons.org/licenses/by/3.0/mailto:[email protected]


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Table of Contents+%er%ie!....................................................................................................................................................($hapter 1: ,rea"ing the *anguage ,arrier !ith *anguage *earning........................................................4

1.1. -oreign *anguage &cuisition.......................................................................................................41.1.1. *1ri%en *2 eaching *1*23.......................................................................................41.1.1.1. he #ea...........................................................................................................................41.1.1.2. hy s t the ,est6 & 7roo8............................................................................................91.1.1.(. istorical e%elopments.................................................................................................;1.1.1.4. &n ystem esign............................................................................................?

1.1.1.4.1. +%er%ie!.................................................................................................................?1.1.1.4.2. &*&> ission 7ro8iles.......................................................................................101.1.1.4.(. &*&> )ser 7ro8iles.............................................................................................191.1.1.4.4. ata &cuisition >trategies...................................................................................1A

1.1.1.5. & Be!er esign: & atari%en &pproach..................................................................1A1.1.2. or# nemonics..................................................................................................................1?

1.1.2.1.


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Overview

n to#ayEs !orl#F the goal o8 'rea"ing the language 'arrier is pursue# on t!o 8ronts: language teachersteaching stu#ents a secon# languageF thus ena'ling humans to manually 'rea" the language 'arrierF an#

computational linguists 'uil#ing increasingly 'etter machine translation systems to automatically 'rea"the language 'arrier.

o!e%erF see importantF un8ul8ille# opportunities on 'oth 8ronts:

n secon# language teachingF amazingly e88icient teaching metho#s ha%e not gone mainstream an# not#ra!n enough attention 8rom computational linguists so that these metho#s coul# 'e automate# an#truly po!er8ul3. -or e=ampleF imagine i8 youEre 'ro!sing a e' page in your nati%e languageF an# ae' 'ro!ser e=tension automatically #etects the topic o8 this page an# inserts rele%ant 8oreignlanguage microlessons in itF so that you can inci#entally learn a 8oreign language !hile 'ro!singinteresting nati%e language in8ormation :3 his >enseli"e G*1#ri%en *2 teachingG !ill 'e the8uture o8 secon# language teaching.

n machine translationF computational linguists only pay attention to computer capa'ilities to processnatural language "no!n as natural language processingF B*73F an# totally ignore human capa'ilities toshare some 'ur#en 8rom the computer in language processingF !hich can lea# to signi8icantly 'etterresults. -or e=ampleF theory an# practice ha%e pro%en that synta= #isam'iguation is a much har#er tas"than !or# sense #isam'iguationF an# there8ore machine translation ten#s to scre! up the !or# or#er o8the translation result i8 the language pair has #isparate !or# or#ersH 'ut !hat i8 machine translationpreser%es the source languageEs !or# or#er in the translation resultF an# teaches the en# user a'out thesource languageEs !or# or#er so that he can manually 8igure out the logic o8 the translation result6 8the en# user is !illing to commit some o8 his o!n natural intelligence in the manmachine Ioint e88ortto 'rea" the language 'arrierF he !ill get the Io' #one 'etter.

here8ore this e'oo" presents emerging i#eas an# implementations in computerassiste# languagelearning $&**3F secon# language rea#ing an# !riting ai#s an# machine translation 3 that stri%e tole%erage 'oth human an# machine language processing potential an# capa'ilitiesF an# !ill re#e8ine the!ay people 'rea" the language 'arrier.

&pproaches !hose titles ha%e an e=clamation mar" 3 are stirring gamechanging technologies !hichare the #ri%ing 8orces 'ehin# this initiati%e.

You can stay in8orme# o8 ne! %ersions o8 this e'oo" 'y su'scri'ing to

http://groups.google.com/group/'l'gcaannounce

an# #iscuss topics in the e'oo" !ith the author an# other rea#ers at

http://groups.google.com/group/'l'gca#iscuss
http://groups.google.com/group/blbgca-announcehttp://groups.google.com/group/blbgca-discusshttp://groups.google.com/group/blbgca-announcehttp://groups.google.com/group/blbgca-discuss


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Chapter 1: Breaking the Language Barrier withLanguage Learning

>ometimes a person !ants to internalize a 8oreign language in or#er to un#erstan# an# generate

in8ormation in that languageF especially in the case o8 ection 1.1.1 a##resses the !henK pro'lem !ith a metho# calle# *1#ri%en *2 teachingKF !hichautomatically teaches you a secon# language !hen youEre 'ro!sing nati%e language !e'sites.

>ection 1.1.2 a##resses the ho!K pro'lem !ith %arious mnemonic #e%icesF all o8 them 8itting neatly!ith the *1#ri%en *2 teachingK 8rame!or".

1.1.1. L1!"riven L2 Teaching# $L1"L2T%

1.1.1.1. The &'ea

A Quick Introduction


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magine i8 youEre 'ro!sing a e' page in your nati%e language *1K3F an# a e' 'ro!ser e=tensionautomatically #etects the topic o8 this page an# inserts rele%ant 8oreign language *2K3 microlessonsin itF so that you can inci#entally learn a 8oreign language !hile 'ro!sing interesting nati%e languagein8ormation :3 -or e=ampleF i8 the e' page is a ne!s story a'out 'as"et'allF the 'ro!ser e=tensioncan insert microlessons a'out 'as"et'all !or#s an# e=pressions in the 8oreign language you !ish to

learn.

his >enseli"e *1#ri%en *2 teachingK !ill 'e the 8uture o8 secon# language teaching.

Topic-Oriented vs. Word-Oriented Teaching

,esi#es inserting 8oreign language microlessons 'ase# on the pageEs o%erall topicF the 'ro!sere=tension coul# e%en insert microlessons a8ter in#i%i#ual !or#s on that page to speci8ically teach these!or#sE 8oreign counterparts. -or e=ampleF i8 a sentence

$hinese 8or e is a goo# stu#ent.K3 appears in a $hinese personEs e' 'ro!serF the 'ro!sere=tension can insert a8ter Ka microlesson that teaches its ) stu#entK.3 #itional in8ormation such as stu#entEspronunciation can also 'e inserte#. &8ter se%eral microlessons li"e this each lesson teaching #i88erenta##itional in8ormation such as e=ample sentencesF relate# phrases an# comparisons to nearsynonyms3Fthe computer can #irectly replace 8uture occurrences o8 K!ith stu#entK:

stu#ent

,ut 'ear in min# that such #irect replacement is not al!ays technically possi'le or pe#agogically!elcomeF especially i8 the !or# 'eing taught is a %er' an# has #i88erent argument structures in the t!olanguages. n practiceF a 8oreign !or# can 'e practice# separately in microlessonsF each lessoncontaining one e=ample sentenceF e.g.

stu#entF : There are 20 students in our class.3

and!ing Am"iguit# in Word-Oriented Teaching

)nli"e topicoriente# teachingF !or#oriente# teaching nee#s to #eal !ith a pro'lem concerning

am'iguous nati%e language !or#s on the e' page. he 'ro!ser e=tension nee#s arti8icial intelligencemore speci8icallyF !or# sense #isam'iguationK3 to #etermine an am'iguous !or#Es inten#e# meaning'ase# on conte=tF an# then teach 8oreign language 8or that meaning. >uch #isam'iguation may not 'eal!ays rightF so the computer shoul# a!$a#s te!! the user $hich meaning is "eing assumed in theteaching.

8 an *1*2 system !ants to a%oi# the pro'lem o8 !or# sense #isam'iguation entirelyF it shoul# usetopicoriente# teaching instea# o8 !or#oriente# teaching.


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What a"out Teaching Grammatica! %no$!edge&

Drammatical "no!le#ge can 'e taught similarly using *1#ri%en *2 teaching. he 'ro!ser e=tensioncoul# #etect a certain grammatical usage in the nati%e language page an# insert a microlesson a8terthat usage to teach its correspon#ing 8oreign language grammatical usage.

L'(L)T in a *u!ti-+eer ,nvironment

8 an *1*2 system inserts 8oreign language microlessons not only into the userEs incoming nati%elanguage communication e.g. a e' page loa#e# into his 'ro!ser3 'ut also outgoing communicatione.g. a message he posts to a 8orum3F all his recipients !ill 'e engage# in language learningF e%en i8they themsel%es #o not install an *1*2 system on their si#e. 7ut another !ayF i8 only one acti%eparticipant in an online community e.g. an C$ chat room or a 8orum3 *1*2izes his outgoingmessagesF all other mem'ers !ill 'e learning the 8oreign language. tEs li"e someone smo"ing in alo''y L no one else !ill sur%i%e the smo"e.

>uch a situation also 8osters language learnersE pro#ucti%e "no!le#geK in a##ition to recepti%e"no!le#geK recepti%eK means a learner can recognize the meaning o8 a !or# !hen he sees or hearsthat !or#F !hile pro#ucti%eK means he can in#epen#ently !rite or say a !or# !hen he !ants to useit3. -or e=ampleF suppose t!o $hineseF &lice an# ,o'F are chatting !ith each otherF an# &lice says:

$hinese 8or e is a goo# stu#ent.K3F 'ut &liceEs *1*2 system trans8orms this outgoing messageFreplacing the $hinese !or# K !ith its


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secon#s as !ell. ,ut this is still science 8ictionF an# !e still ha%e to acuire "no!le#ge !ith naturalmeans: our eyesF ears an# slo!ish memory.

act ): An A00ective Constraint in oca"u!ar# Ac/uisition

,esi#es the a'o%ementione# cogniti%e limitF !e humans also ha%e an emotional threshol# in acuiring

ne! "no!le#ge. -or e=ampleF a 'eginner o8 a 8oreign language is not li"ely !illing to sit there all #ayan# memorize a #ictionary o8 this 8oreign language. >imilarlyF he !oul# 8in# it %ery #i88icult to rea# anarticle in this 8oreign language inten#e# 8or nati%e rea#ersF as most !or#s in such an article !oul# 'eun8amiliar to him. hese t!o e=amples tell us that !e simply canEt 8orce a person to #igest a largenum'er o8 un8amiliar 8oreign language !or#s at once.

(ivide and Time!# Con/uer

,ase# on -act 2 a'o%eF !e can conclu#e that the %ast %oca'ulary o8 a 8oreign language must 'e #i%i#e#into small piecesK an# taught to a learner one piece at a time e.g. one !or# at a time3F an# !eE# 'etterchoose a time !hen the learner is most moti%ate# to learn such a piece. -or e=ample:

!hen the learner is playing a computer game or participating in a %irtual reality !orl# such asSecond LifeF la'el o'Iects in that game or %irtual !orl# !ith 8oreign language #escriptionsH

!hen the learner is !atching a mo%ieF sho! 'oth nati%e language an# 8oreign language su'titleson the screenH

!hen the learner is using a computer programF sho! the programEs user inter8ace in a 8oreignlanguageH

!hen the learner is interacting !ith the real !orl#F sho! 8oreign language la'els on roa# signsF'ill'oar#sF pro#uctsF o88icial #ocumentsF etc.H

!hen the learner is 'ro!sing a nati%e language e' pageF insert 8oreign language microlessonsrele%ant to the pageEs topic or certain !or#s o8 the page.

&mong the a'o%e e=amplesF it is o'%ious that the last e=ampleF *1*2F pro%i#es the !i#est spectrumo8 !or# teaching opportunities.

1.1.1.,. -istorical "evelopents

'123s: 4o""ins Bur!ing5s (ig!ot *ethod

n the 1?90s &merican anthropologist an# linguist Co''ins ,urling 8irst intro#uce# the metho# o8gra#ually intro#ucing 8oreign language ingre#ients in a nati%e language story'oo" to teach 8oreignlanguage in his seminal paper Some Outlandish Proposals for the Teaching of Foreign Languages.

,urling calle# it the #iglot metho#K an# !as inspire# 'y a Learning !hinese'oo" series pu'lishe#'y Yale )ni%ersity 7ressF !here ne! $hinese characters gra#ually replace# Comanize# $hinese in ate=t'oo".

,urlingEs metho# reuires a human translator to manually trans8orm a nati%e language story'oo" insuch a !ay thatF as a rea#er rea#s onF he !oul# see more an# more !or#s an# phrases e=presse# in a8oreign languageF an# e%entually !hole sentences an# paragraphs too. his 8orces the rea#er to loo" upne!ly encountere# 8oreign language elements in a #ictionary or guess their meanings 'ase# onconte=tF there8ore gra#ually pic"ing up that 8oreign language as he e=plores the story.


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>ince then the metho# is also "no!n as mi=e# te=tsKF 'ilingual metho#KF co#e s!itchingKFsan#!ich techniueK or #iglot !ea%eK in the 8oreign language teaching research community.o!e%er the metho# has ne%er 'ecome a mainstream metho#.

'113s 6 +resent: (ig!ot Books7 ,"ooks and ideos on the *arket

here are e#ucational materials using manually prepare# #iglot te=ts on the 8oreign language teachingmar"etF 'ut they ha%e ne%er gone mainstream. -or e=ampleFPo"er#lide!!!.po!ergli#e.com3 sellsinteracti%e #iglot e'oo"sH 7ro8essors Ji Yuhua 3 an# Mu Nichao 3 sell #iglot %i#eos title#

Three Little Pigs and Step"ise $nglish %&in $hina.

)333s 6 +resent: Automatic L'(L)T Theories and 8#stems

n Bo%em'er 2004 in#epen#ently came up !ith the #iglot i#ea )senet postan# threa#3F an# again in&pril 200; )senet postan# threa#3. -rom the 'eginning ha%e 'een researching it as an automaticsystem e.g. a e' 'ro!ser e=tension3 using computational linguistics an# natural language processing

$*/B*73. aIor aspects o8 this research are presente# in >ection 1.1.1.1 he #eaK. -or e=ample:1. propose a ne! *1*2 para#igmF topicoriente# teachingKF to completely a%oi# the pro'lem

o8 !or# sense #isam'iguationH2. propose that !or#oriente# teaching must al!ays tell the user !hich sense is assume# !hen

teaching 8oreign language 8or an am'iguous nati%e language !or#H(. propose that !e can put *2 teachings an# practices in microlessonsK separately 8rom a e'

pageEs original *1 te=tF so that !e !onEt ha%e linguistic pro'lems commonly encountere# !hent!o languages are mi=e# in the same sentence.

!ill also present an e=ample *1*2 system #esign in >ection 1.1.1.4 &n ystemesignKF co#e name'TL'S %'cti(e Target Language 'c)uisition S*stem&.

here are also other e88orts to implement an automatic *1*2 system in recent years:

+e,-oca,http://!e'%oca'.source8orge.net/ 3 is a "in# o8 -ire8o= a##on Dreasemon"ey userscript3. t can 'e classi8ie# as !or#oriente# teachingK see opic+riente# %s. or#+riente#eachingK in >ection 1.1.1.13F an# only #isam'iguates !or#s 'y parto8speech clues e.g. a!or# a8ter K must 'e a %er'/a#%er' rather than a noun/a#Iecti%eF so canK a8ter K must 'e inits au=iliary %er' senseF 'e a'le toKF rather than its noun senseF containerK3H other!ise it !illnot teach or practice 8oreign language 8or am'iguous !or#s at all.

mingalinghttps://a##ons.mozilla.org/en)>/8ire8o=/a##on/mingaling/ 3 is a -ire8o=e=tension that can also 'e classi8ie# as !or#oriente#K *1*2. t simply calls Doogleranslate to translate nati%e language !or#s on a e' page to a 8oreign language. o!e%erF in

case a nati%e language !or# is am'iguousF it #oes not tell the user !hich sense is assume# inthe teachingF so the user may 'e taught a !rong 8oreign language !or# an# !ill not "no! a'outit !hen such a misteaching happens.

!haracteri/erhttps://a##ons.mozilla.org/en)>/8ire8o=/a##on/characterizer/ 3 is a -ire8o=e=tension that aims to teach $hinese or Japanese characters 'y putting them in the nati%elanguage e' page youEre 'ro!sing. t is also a !or#oriente# *1*2 system. o!e%erF incase a nati%e language !or# is am'iguousF it !ill simply choose a ran#om sense an# teach youthe $hinese/Japanese character 8or that senseF an# you !ill ne%er "no! i8 a misteachinghappens.
http://www.power-glide.com/https://groups.google.com/group/list.linguist/msg/6c54ecaa5e314696?hl=enc54ecaa5e314696https://groups.google.com/group/list.linguist/browse_frm/thread/2f3af9e027a773ca/6c54ecaa5e314696?hl=enc54ecaa5e314696https://groups.google.com/group/list.linguist/msg/d30c56cc55471684?hl=enhttps://groups.google.com/group/list.linguist/browse_frm/thread/130da4990b7185a6/d30c56cc55471684?hl=end30c56cc55471684http://webvocab.sourceforge.net/https://addons.mozilla.org/en-US/firefox/addon/ming-a-ling/https://addons.mozilla.org/en-US/firefox/addon/characterizer/http://www.power-glide.com/https://groups.google.com/group/list.linguist/msg/6c54ecaa5e314696?hl=enc54ecaa5e314696https://groups.google.com/group/list.linguist/browse_frm/thread/2f3af9e027a773ca/6c54ecaa5e314696?hl=enc54ecaa5e314696https://groups.google.com/group/list.linguist/msg/d30c56cc55471684?hl=enhttps://groups.google.com/group/list.linguist/browse_frm/thread/130da4990b7185a6/d30c56cc55471684?hl=end30c56cc55471684http://webvocab.sourceforge.net/https://addons.mozilla.org/en-US/firefox/addon/ming-a-ling/https://addons.mozilla.org/en-US/firefox/addon/characterizer/


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pol*glophttps://a##ons.mozilla.org/en)>/8ire8o=/a##on/polyglop/ 3 is a -ire8o= e=tension thatis also a !or#oriente# *1*2. n case a nati%e language !or# is am'iguousF it !ill choose asense at ran#om an# teach that sense in 8oreign language. You !ill ne%er "no! i8 a misteachinghappens.

pol*glothttps://chrome.google.com/!e'store/#etail/plpI"Ipl"n"nmh8h"Igc8go8clmlnine 3 is a$hrome e=tension that also #oes !or#oriente# *1*2 'y calling Doogle ranslate to

translate certain nati%e language !or#s in your $hrome 'ro!ser. *i"e e=tensions a'o%eF it !illnot tell you !hich sense is assume# !hen teaching 8oreign language 8or an am'iguous nati%elanguage !or#F so you !ill ne%er "no! i8 a misteaching happens.

&s you seeF all these other e88orts liste# a'o%e are !or#oriente# *1*2 systems 'ut they #onEt tellyou !hich sense is chosen 8or 8oreign language teaching !hen a nati%e language !or# is am'iguousF soyou !ill ne%er "no! i8 a misteaching happens.

'elie%e an automatic *1*2 system shoul# either 'e topicoriente# so that it #oesnEt in%ol%e thepro'lem o8 !or# sense #isam'iguation at all3F or 'e !or#oriente# 'ut al!ays tell the user !hich senseis chosen 8or teaching.

1.1.1./. An 0aple )ste "esign

1.1.1./.1. Overview

,elo! !ill present the #esign o8 a 8ictional *1*2 systemF co#e name ATLA8 9Active TargetLanguage Ac/uisition 8#stem. t consists o8 a $hrome e=tension an# associate# open stan#ar#s.

$urrently it is only 8ocuse# on !or#oriente# teaching. o!e%erF the t!o para#igms !or#oriente#teaching an# topicoriente# teaching3 actually share much in commonF an# the system has 8eatures that!ill support topicoriente# teaching at a later time. hese 8eatures !ill also 'e presente#.

Operation

he $hrome e=tension !ill loa# t!o 8iles 'e8ore actual 8oreign language teaching ta"es place: 13 an;ATLA8 teaching mission pro0i!e3 strategy is reuire# 8or
https://addons.mozilla.org/en-US/firefox/addon/polyglop/https://chrome.google.com/webstore/detail/plpjkjplknknmhfhkjgcfgofclmlninehttps://addons.mozilla.org/en-US/firefox/addon/polyglop/https://chrome.google.com/webstore/detail/plpjkjplknknmhfhkjgcfgofclmlnine


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&*&>. e !ill use a %ery simple yet e88ecti%e > approach that assumes a !or#Es inten#e# sense is#etermine# 'y the topicK o8 the !or#Es conte=tF an# the topicK is in turn #etermine# 'y !hat other!or#s occur in that conte=t. -or e=ampleF i8 the userEs nati%e language *13 is ection 1.1.1.4.2&*&> ission 7ro8ilesK.

>ince no > strategy is per8ectF microlessons must e=plicitly tell the user !hich !or# sense is 'eingassume# in the teaching.

O00-Topic Teaching

n principleF !or#oriente# teaching !ill teach a 8oreign language !or# only i8 its correspon#ing nati%elanguage !or# appears in the userEs e' 'ro!ser. ,ut !hat i8 nati%e language !or#s #e8ine# in ateaching mission pro8ile ne%er appear in the 'ro!ser6 -or e=ampleF !hat i8 !e ha%e a mission pro8ilethat teaches 8oreign language 8or 'as"et'all !or#sF 'ut the user ne%er happens to 'ro!se e' pagesrelate# to 'as"et'all6 n that case the teaching mission !oul# ne%er 'e per8orme# success8ully. oa##ress this pro'lemF !e shoul# let &*&> occasionally teach microlessons unre!ated to a We"page5s $ordsF at the 'ottom o8 that pageF i8 &*&> 8in#s it #i88icult to 8in# normal teachingopportunities.

1.1.1./.2. ATLA 3ission +rofiles

&n &*&> teaching mission pro8ile mission pro8ileK 8or short3 is a plain te=t 8ile !ith the 8ilee=tension .mission.IsonK that #e8ines !hat !ill 'e taught to the user an# ho!: !hich nati%e language!or#s !ill trigger 8oreign language teachingF !hat content !ill 'e #isplaye# to the user in such ateaching i.e. the microlessons3F an# #ata that help the e=tension to #isam'iguate am'iguous nati%elanguage !or#s so that &*&> can try to teach 8oreign language 8or the right sense.

A 8amp!e *ission +ro0i!e

!ill 8irst sho! you a sample mission pro8ile sample.mission.IsonK an# then e=plain it.

{ "dataFormat": "ATLAS Teaching Mission Profile Format 0.01",

"title": "School-related ords !nglish -# $hinese%","descri&tion": "For nglish s&ea'ers to learn school-related ords in $hinese.",

"a(thors":)

"*i+(an ao !+aoi+(angmail.com%"/,"&art+": "+aoi+(angmail.com","date": "1 Fer(ar+ 01",


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"license": "$reati2e $ommons Attri(tion 3.0 License",

"L1": "nglish","L": "Sim&lified $hinese",

"&aradigm": "ord-oriented teaching","ot$onte4t5indoSie": {"(nit": "ord", "2al(e": 306,

"le4emes":){

"le4eme78": "teacher1","inflectedForms": )"teacher", "teachers"/,"microLessons": )"A teacher is a !9iosh;%.", "..."/

6,{

"le4eme78": "st(dent1","inflectedForms": )"st(dent", "st(dents"/,"microLessons": )"A st(dent is a !4(n%.", "..."/6,{

"le4eme78": "teach1","inflectedForms": )"teach", "teaches", "teaching", "ta(ght"/,"microLessons": )"To teach something is to !9i?o% something.",

"..."/6,{

"le4eme78": "learn1","inflectedForms": )"learn", "learns", "learning", "learned", "learnt"/,"microLessons": )"To learn something is to !4(


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/6

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he structure# 8ormat you see in the a'o%e sample mission pro8ile is calle# =8O>Ja%a>cript +'IectBotation3. #eallyF mission pro8iles shoul# 'e e#ite# !ith a #e#icate# e#itor programF 'ut i8 youEre

'ra%e enough to e#it it #irectlyF you shoul# learn J>+B 8irstF !hich is %ery uic" to learn.

dataormatspeci8ies the 8ormat %ersion that this mission pro8ile uses.

tit!eF descriptionF authorsF datean# !icenseare #escripti%e items 8or human users to rea#H they #onEta88ect &*&>Es 'eha%ior.

part#is a 8eature that allo!s authors o8 multiple mission pro8iles to coor#inate their e88orts so that theirmission pro8iles can %irtually !or" as a single mission. his !ill 'e 8urther e=plaine# later. 8 you #onEt!ant to coor#inate !ith other authorsF use a uniue i#entity o8 yours e.g. email a##ress3 as the party%alue.

L'an# L)respecti%ely speci8y the nati%e an# 8oreign language in this teaching mission. *1 also tells&*&> !hether the nati%e language uses spaces to #elimit !or#s. -or e=ampleF 3 algorithm ho! large a conte=t it shoul# loo" at to #etermine a !or#Es sense. 8*1 is a space# language e.g. ize !ill 'e speci8ie# in terms o8 !or#sH

other!ise e.g. >impli8ie# $hinese3 it !ill 'e speci8ie# in terms o8 characters.

totConte?tWindo$8i@eF use# !ith para#igm O topicoriente# teachingKF tells &*&> ho! large aconte=t it shoul# loo" at to #isco%er topics that may trigger *2 teaching. 8 *1 is a space# languagee.g. ize !ill 'e speci8ie# in terms o8 !or#sH other!ise e.g. >impli8ie#$hinese3 it !ill 'e speci8ie# in terms o8 characters.

!e?emesspeci8ies a list o8 *1 le=emes actually le=ical itemsKF as phrases are also allo!e#3 that coul#trigger *2 teaching.


13/36

topicH i8 para#igm O topicoriente# teachingKF another itemF topicKF !ill 'e use# 8or the computerto uniuely i#enti8y a topic among mission pro8iles o8 the same party an# *1.

Word 8ense (isam"iguation $ith ;$eight< and ;dependenc# to interpret the !or# 'assK as the le=eme'assP8ish3KF i8 either the le=eme sea1K or 8ishing1K appears near'yH or interpret it as'assPmusic3KF i8 either music1K or song1K appears near'y. o #o thisF !e gi%e sea1K an#8ishing1K a !eightK o8 10F an# gi%e 'assP8ish3K a #epen#encyK o8 10F !hich means !hether'assP8ish3K !ill 'e assume# #epen#s on !hether other -ishingK le=emes e.g. sea1K an#8ishing1K3 in the same conte=t constitute a total !eight o8 at least 10. n other !or#sF either sea1K or8ishing1K or 'oth o8 them must appear near'y to let &*&> interpret the !or# 'assK as the le=eme'assP8ish3K an# teach this le=emeEs *2 microlessons. e treat music1KF song1K an#'assPmusic3K similarly so that at least one among music1K an# song1K must 'e present in the

conte=t to let &*&> interpret the !or# 'assK as 'assPmusic3K an# teach *2 microlessonsaccor#ingly.

8 a !or# is unam'iguous e.g. musicK3F its le=eme e.g. music1K3 can ha%e a #epen#encyK o8 0F!hich means it #oesnEt reuire any other le=eme 8rom the same topic to appear near'y to help#isam'iguate itF as it is unam'iguous any!ay.

8 a le=eme #oes not suggest a topic as much as other le=emes #oF it can ha%e a smaller !eight e.g.!eight O 53 8or that topic than other le=emes. -or e=ampleF the occurrence o8 'all penK in a e' pagemay not suggest the presence o8 the topic >choolingK as much as the occurrence o8 teacherK !oul#Fso 'all penK may ha%e a smaller !eight than teacherK 8or that topic. >imilarlyF there can 'e le=emes

that ha%e a greater !eight than othersF i8 they more strongly suggest a topic.

&lso note that the microlessons 8or 'assP8ish3K an# 'assPmusic3K e=plicitly tell the user !hich'assK is 'eing taught in 8oreign languageF 'y a##ing 8ish3K an# music3K in the lesson content. hisis in accor#ance !ith the !or#oriente# teaching must e=plicitly tell the user !hich !or# sense is'eing assume# !hen teaching 8oreign language 8or an am'iguous nati%e language !or#K principle propose# in >ection 1.1.1.1 he #eaK.

4A!!-0or-A!!< W8( vs. ;A!!-0or-One< W8(

7re%iously !e #emonstrate# topicsK !ithin !hich le=emes hint at each other in !or# sense

#isam'iguation >3F e.g.

{"to&icTitle": "Fishing","memers":)

{"le4eme78": "sea1", "eight": 10, "de&endenc+": 06,{"le4eme78": "fishing1", "eight": 10, "de&endenc+": 06,{"le4eme78": "assC!fish%", "eight": 10, "de&endenc+": 106

/6


15/36

e call this all8orall >K. o!e%erF i8 necessaryF !e can e%en #esign topics solely 8or the purposeo8 hinting at only one le=eme in >. -or e=ampleF !e can mo#i8y the a'o%e topic as:

{"to&icTitle": "Hass !fish%","memers":

){"le4eme78": "sea1", "eight": 10, "de&endenc+": IIIII6,{"le4eme78": "fishing1", "eight": 10, "de&endenc+": IIIII6,{"le4eme78": "assC!fish%", "eight": 10, "de&endenc+": 106

/6

-irstF notice the topicitle has 'een change# to ,ass 8ish3KF 'ecause this ne! topic is solely 8orhinting at the le=eme 'assP8ish3K.

>econ#lyF notice 'oth sea1K an# 8ishing1K ha%e a #epen#encyK o8 ?????F !hich means theyEll ne%er'e acti%ate# 8or *2 teaching at least 8rom this topic3. ,ut they ha%e a normal !eight o8 10F !hich

means they can help acti%ate 'assP8ish3K 8or *2 teaching.

+arties

part#is a 8eature that allo!s authors o8 multiple mission pro8iles to coor#inate their e88orts so that theirmission pro8iles can %irtually !or" as a single mission. 8 you #onEt !ant to coor#inate !ith otherauthorsF use a uniue i#entity o8 yours e.g. email a##ress3 as the party %alue..

8 multiple mission pro8iles speci8y the same party %alueF their le=emes are mutually recognize# an#there8ore their le=emes can 'e merge#. ,esi#esF their !eight an# #epen#ency %alues are in accor#ance!ith the same #esign stan#ar# e.g. they !oul# all use 10 as a stan#ar# !eight3. -urthermoreF they !ill

ha%e consistent !ot$onte=tin#o!>ize an# tot$onte=tin#o!>ize %alues.

8upporting Topic-Oriented Teaching

&lthough currently this e=ample system #esign is 8ocuse# on !or#oriente# teachingF it !ill 'e easy tosupport topicoriente# teaching 'ecause the t!o para#igms share much #ata in common.

-or e=ampleF consi#er the >chooling topic in our 8irst sample mission pro8ile:

{"to&icTitle": "Schooling","memers":

){"le4eme78": "teacher1", "eight": 10, "de&endenc+": 06,{"le4eme78": "st(dent1", "eight": 10, "de&endenc+": 06,{"le4eme78": "lac'oard1", "eight": 10, "de&endenc+": 06,{"le4eme78": "teach1", "eight": 10, "de&endenc+": 06,{"le4eme78": "learn1", "eight": 10, "de&endenc+": 06,{"le4eme78": "te4too'1", "eight": 10, "de&endenc+": 06

/6

e can #isa'le !or#oriente# teachingF 'y changing all #epen#ency %alues to a large num'erF sayF


16/36

?????. hen !e can intro#uce a topicle%el #epen#ency %alue an# micro*essonsF so that the topic itsel8can 'e acti%ate# 8or *2 teaching:

{"to&icTitle": "Schooling","to&ic78": "schooling","memers":

) {"le4eme78": "teacher1", "eight": 10, "de&endenc+": IIIII6,{"le4eme78": "st(dent1", "eight": 10, "de&endenc+": IIIII6,{"le4eme78": "lac'oard1", "eight": 10, "de&endenc+": IIIII6,{"le4eme78": "teach1", "eight": 10, "de&endenc+": IIIII6,{"le4eme78": "learn1", "eight": 10, "de&endenc+": IIIII6,{"le4eme78": "te4too'1", "eight": 10, "de&endenc+": IIIII6

/,"de&endenc+": 30,"microLessons": )"Lesson 1...", "Lesson ...", .../

6

his ne! topic means i8 at least three le=emes in this topic are present in a conte=tF &*&> can insertthe topicle%el micro*essons into that conte=t to teach schoolingrelate# *2 lessons.

topicK gi%es the topic a uniue !hich !ill help &*&> recor# the userEs learning progress 8orthis topicEs microlessons.

Bote that a mission pro8ile 8or topicoriente# teaching !oul# usually use a larger conte=t !in#o! sizetot$onte=tin#o!>ize3 than mission pro8iles 8or !or#oriente# teaching !ot$onte=tin#o!>ize3F'ecause the #etermination o8 a large portion o8 te=tEs topic reuires a scan !in#o! larger than that o8the #etermination o8 a !or#Es sense.

Bote that a mission pro8ile can choose either !or#oriente# teaching or topicoriente# teaching %ia thepara#igmK item3F 'ut not 'othF 'ecause running 'oth para#igms at the same time !oul# in%ol%e

unnecessary a##itional engineering comple=ity to han#le con8licts 'et!een the t!o.

1.1.1./.,. ATLA 5ser +rofiles

&n &*&> user pro8ile user pro8ileK 8or short3 is a plain te=t 8ile !ith the 8ile e=tension .user.IsonKthat speci8ies a userEs i#entityF pre8erences an# learning recor#s.

A 8amp!e ser +ro0i!e

!ill 8irst sho! you a sample user pro8ile sample.user.IsonK an# then e=plain it.

{"dataFormat": "ATLAS Jser Profile Format 0.01",

"name": "*i+(an ao","more7nfo": "mail: +aoi+(angmail.com",

"&references":{

"learnSame5ordFromM(lti&leParties": false,


17/36

"microLesson8ensit+": {"(nit": "ord", "2al(e": 10066,

"otLearningKecords":)

{"L1": "nglish","L": "Sim&lified $hinese",

"&art+": "+aoi+(angmail.com","le4eme78": "teacher1","lemma": "teacher","microLessons$om&leted": 1

6,{

...6

/,

"totLearningKecords":)

{"L1": "nglish","L": "Sim&lified $hinese",

"&art+": "+aoi+(angmail.com","to&ic78": "schooling","microLessons$om&leted": 1

6,{

...6

/6

he structure# 8ormat you see in the a'o%e sample mission pro8ile is calle# =8O>Ja%a>cript +'IectBotation3. #eallyF user pro8iles !ill 'e han#le# 'y &*&> automaticallyF 'ut i8 youEre 'ra%e enough toe#it it manuallyF you shoul# learn J>+B 8irstF !hich is %ery uic" to learn.

dataormatspeci8ies the 8ormat %ersion that this user pro8ile uses.

namean# moreIn0oi#enti8y the user.

!earn8ameWordrom*u!tip!e+artiesis a 'oolean %alue that speci8ies !hether the user is !illing tolearn lessons a'out the same !or# o88ere# 'y multiple parties. >ee partyK in >ection 1.1.1.4.2 &*&>ission 7ro8ilesK. 8 8alseF lessons 8or the same !or# o88ere# 'y ne! parties !ill 'e s"ippe#.

microLesson(ensit#speci8ies the #istance 'et!een t!o a#Iacent microlessons inserte# into a e'page. t means ho! 8reuently &*&> shoul# insert microlessons.

$otLearning4ecordsis a list o8 le=emes !hose *2 microlessons ha%e 'een or are 'eing taught to theuser %ia !or#oriente# teaching.

totLearning4ecordsis a list o8 topics !hose *2 microlessons ha%e 'een or are 'eing taught to theuser %ia topicoriente# teaching.


18/36

1.1.1././. "ata Acuisition trategies

>ince &*&> relies on mission pro8iles to pro%i#e *1#ri%en *2 teaching in the userEs e' 'ro!serF itis %ery important to #iscuss ho! such mission pro8iles can 'e pro#uce#. DenerallyF see t!oapproaches:

Let 8econd Language Teachers *anua!!# +repare *ission +ro0i!es

eachers can #esign le=emes an# topics 'ase# on:

!hat topics their stu#ents most li"ely 'ro!se online e.g. sportsF entertainmentF 8ashionFgamingF etc.3H

!hat topics theyEre going to teach in the near termH

generalpurpose topics that are li"ely to appear in any e' page topics that group relate#generalpurpose !or#sF such as a topic that groups i8KF thenK an# elseKF or another topic thatgroups as"K an# ans!erK.

eachers can also 8orm !or"groups that pool their e88orts togetherF using the same part#%alue tocoor#inate their mission pro8iles see >ection 1.1.1.4.23.

Let Computationa! Linguists Automatica!!# Generate *ission +ro0i!es

t is e%en possi'le 8or computational linguists to automatically generate &*&> mission pro8iles 8romrele%ant #ata sources. -or e=ampleF a le=eme can correspon# to a i"ipe#ia articleF an# on that article acomputer program can automatically 8in# out lin"s to topically relate# le=emes articles3F thusautomatically generating topics that group relate# le=emes together. i"ipe#ia also connectsmultilingual %ersions o8 the same le=eme article3 togetherF !hich can 'e use8ul 8or automaticallygenerating *2 microlessons 8or a gi%en le=eme.

1.1.1.6. A 7ewer "esign: A "ata!"riven Approach

n ay 2012 realize# that a #ata#ri%en approachF !hich #oesnEt reuire language teachers tomanually pro#uce mission pro8ilesKF is more practical. ,elo! is my ne! i#ea poste# to $orporalistan# other computational linguistics mailing lists. inten# to #e%elop this i#ea into a $hrome 'ro!sere=tension.

"Language Immersion for Chrome", and a Better Idea

Google's "Language Immersion for Chrome"

Recently a Chrome browser extension called "Language Immersion for Chrome" has

been much publicized. Developed by "Use All Five Inc." on behalf of Google, the

extension translates certain words and phrases on the Web page you're browsing to

a foreign language via Google Translate, for the purpose of helping you learn

that foreign language while browsing the Web.

I have been researching this kind of thing for years, and one of my main

standpoints is machine translation shouldn't be used in serious language learning

as it is error-prone: it takes a learner a great effort to memorize a piece of


19/36

erroneous knowledge, another great effort to "unlearn" this wrong knowledge and

yet another great effort to "relearn" the right knowledge.

But I do understand online machine translation services like Google Translate and

Bing Translator are so readily available that directly using them to do the

translation can minimize development costs. Upon seeing this news, I asked

myself: "Can we use a kind of freely available, manually prepared data, instead

of machine translation, to do this better?" And the answer is YES!

A Better Idea

Imagine if we have a database of manually-translated bilingual sentence pairs

(such as those multilingual movie subtitle files on those subtitle websites, and

those famous quotations on wikiquote.org), e.g.

(German) Er ist ein guter Schler.

(English) He is a good student.

Now if a German wants to learn English, and he happens to be browsing a German

Web page that contains the German word "Schler" (student), and the computer

finds out that this German word also occurs in a bilingual sentence pair like the

above. Now, the computer can teach English for this German word, by inserting theabove bilingual sentence pair into that Web page, like an embedded advertisement.

This way, the German will learn the English word "student", and better yet, learn

it in a bilingual sentence pair! This means he will not only learn the word

"student" alone, but also its syntax, semantics and pragmatics, all implied by

this example sentence. As to phonetics, the computer can use text-to-speech to

read aloud the English sentence, or display some kind of pronunciation guide

above or alongside the English sentence (see my recent project "Phonetically

Intuitive English" for such a pronunciation aid:

https://sites.google.com/site/phoneticallyintuitiveenglish/).

That's the basic idea. But of course we can further refine this idea. For

example, if there are multiple bilingual sentence pairs containing "Schler", the

computer can prefer a pair that contains words that appear near "Schler" on theWeb page (i.e. context words). This would be very useful if the word in question

(Schler) is ambiguous.

Besides bilingual sentence pairs, we may also explore multilingual data from

Wiktionary and Wikipedia, although their usage may not be as straightforward as

the model discussed above. I leave this as homework for the reader.

I also intend to develop a Chrome extension based on the idea discussed above :-)

Best Regards,

Ziyuan Yao

1.1.2. (or' 3neonics

he *1#ri%en *2 teaching *1*23 metho# #iscusse# in >ection 1.1.1 alrea#y implies an approachto !or# memorization: 'y repetition a ne! !or# is taught an# practice# in a series o8 microlessons'e8ore it is consi#ere# learne#3. Cesearch into more sophisticate# mnemonics has un%eile# metho#sthat can ser%e as po!er8ul 8orce multipliers 8or *1*2F !hich !ill 'e presente# in the 8ollo!ingsections. &mong themF 7honetically ntuiti%e


20/36

recommen#e# 'y this e'oo" as ;essentia! mnemonicsection 1.1.2.1.(3.

+honetica!!# Intuitive ,ng!ish 9essentia!:emorizing a !or# in terms o8 sylla'les ta"es 8ar lesse88ort than in terms o8 lettersF an# there8ore pronunciation as a more compresse# 8orm than spelling is a"ey mnemonic. >ection 1.1.2.1.1 7honetically ntuiti%e ote:7honetically ntuiti%e * &n#i"a ,asicKH in practiceF a 'ro!ser a##on that sho!s


21/36

7< te=t in a 'ro!ser !ill al!ays en8orce such 8onts 8or such te=t to ensure goo# ren#ering3:

Aquick brown fox jumps over thelazy do!

he a'o%e e=ample sho!s pronunciation in a %ery %er'ose mo#e: &KF uKF cKF oKF eKF tKF aK an#yK are assigne# #iacritics to #i88erentiate 8rom their #e8ault soun# %aluesH !K an# hK ha%e a short 'ar

!hich means theyEre silentH multisylla'le !or#s such as o%erK an# lazyK ha%e a #ot to in#icate stress.>uch a mo#e is inten#e# 8or a nonnati%e 'eginner o8


22/36

PHONETICALLY INTUITIVE ENGLISH 2.0

GENERAL MARKS (APPLY TO BOTH VOWEL AND CONSONANT LETTERS)

PIE2 mark R!mark E"am#$!

D!%a&$' a$&!

U&a$$* +m,-! &/$! /!!ar*. Dra1/ a+! +1!$ $!-!r a3 !3 ,3 + a/ & %+r4+r' +1!$ 5653 5753 5853 595 (US: 5; ar! a! a+! '1+ aa!/' $!-!r &4 a ++3 ,' m!a/ 5& m+' '*#,a$+/+/a/' a$&!? a/ ! ra1/ a+! !r'a,/ +/+/a/' $!-!r &4 a @ a/ #. bat ? quick

S!+/ar* a$&! 8Dra1/ !$+1 +r a+! a +/+/a/' $!-!r '+ r!#r!!/' &&a$$* '4! !+/ m+' '*#,a$a$&! %+r '4a' +/+/a/' $!-!r3 !.@. 55 %+r +r @3 5k5 %+r 3 5Z5 %+r /3 55 %+r %3 5[5 %+r'3 55 %+r 3 5\5 %+r ".

soldier 3 class 3 sin 3 of3 thin 3 is3 example

T!r',ar* a$&! 88Dra1/ !$+1 +r a+! a +/+/a/' $!-!r '+ r!#r!!/' &&a$$* '4! '4,r m+' '* #,a$a$&! %+r '4a' +/+/a/' $!-!r3 !.@. 5]5 %+r '3 5%5 %+r @ +r # (,/ +r!r '+ a$,@/ 1,'4 @ ,/'4, a!3 # 4a /+ !+/ar* a$&!)3 5'5 %+r 3 55 %+r ".

this 3 couh 3 phone 3 booked3 xanadu

R+$+r! r^_? r^W4!/ r_55 a/ r5Xion3 ma?hine? vers@ion

STRESS MARK (BELOW A SYLLABLE>S PRIMARY VOWEL LETTER)

PIE2 mark R!mark E"am#$!

Pr,mar* 'r! Dra1/ !$+1 '4! 'r!! *$$a$!> #r,mar* +1!$ $!-!r. pronuncition


23/36

Wh# +I,

o learn a ne! !or#F t!o tas"sF among othersF are in%ol%e#: learning its pronunciation an# its spelling.hese t!o tas"s are relate#F an# choosing !hich to #o 8irst ma"es a lot o8 #i88erence. 8 !e learnspelling 8irstF !eE# 'e memorizing a usually long3 seuence o8 lettersF !hich is as te#ious asremem'ering a long telephone num'er. ,ut i8 !e learn pronunciation 8irstF !eE# 'e memorizing a much

shorter seuence o8 sylla'lesF !hich can 'e #one in a 'reezeH then pronunciation can ser%e as a goo#catalyst 8or the su'seuent memorization o8 spelling.

here8ore pronunciation plays a prominent role in !or# acuisitionF an# it is !orth!hile 8in#ing out agoo# metho# to learn it.

& 'ig #ra!'ac" o8 7&is thatF 'ecause it sho!s pronunciation separate!#8rom spellingF it gi%es theuser a chance to s"ip learning pronunciation at all. his is especially the case !hen the user encountersan un"no!n !or# in rea#ing an article: at that momentF the user cares most a'out the meaningo8 thatne! !or#F not the pronunciationF as he #oesnEt ha%e a nee# to hear or say that !or# in real li8e in thenear 8uture. here8ore he is %ery li"ely to s"ip learning the !or#Es true pronunciation in a #ictionaryF'ut instea# ma"e a guessed pronunciationon his o!n. a"ing a guesse# pronunciation !ill then lea#to t!o ne! pro'lems: a3 'ecause the user is a nonnati%e spea"er o8


24/36

http://en.!i"ipe#ia.org/!i"i/$om'iningPcharacter 3

Pronunciation respelling for $nglishL comparison o8 respelling schemes in maIor #ictionarieshttp://en.!i"ipe#ia.org/!i"i/7ronunciationPrespellingP8orPotes

he general i#ea o8 representing a letterEs %arious soun# %alues 'y a##itional mar"s is pro'a'ly as ol#as #iacritics.

&merican #ictionaries 'e8ore the 20

th

century sho!e# #iacritical mar"s #irectly a'o%e hea#!or#s toin#icate pronunciation 8or nati%e rea#ers though not necessarily %er'osely3. hey ha%e 'een replace#'y separate transcription schemesF such as the 7& an# respelling systems.

#ing #iacritics to


25/36

tra#itional $hinese musical instrument suona3 !hich !as 8eature# in an elementary school music

class an# 'ears a similar pronunciation to the sonaK part o8 sonataK. t shoul# also 'e note# thatF assai# earlierF sometimes !or#s ser%ing as mnemonics are not necessarily relate# to the !or# to 'ememorize# in meaning. -or e=ampleF to memorize the !or# +scarKF !e can associate it !ith t!o"no!n !or#sF +>K operating system3 an# carKF although they ha%e nothing to #o !ith +scar inmeaning.

here8ore it is use8ul to let people contri'ute nati%e language'ase# an# target language'ase#mnemonics colla'orati%ely online. i"tionary might 'e a potential site 8or such colla'oration.

1.1.2.1.,. (h) Are The) 0ssential* A +roof

,elo! !ill pro%e !hy 7honetically ntuiti%e


26/36

ran#omly 8orme#. e can trace antto .Dmc. amait3oaio88F a!ayK S maitcutK3. &lthoughamait3ohas a connection 'et!een its 8orm an# meaningF this connection gets lost !hen amait3o8urther trans8orms to antF !hich can 'e consi#ere# as ran#omize#K. >imilarlyF ,log#eri%es 8rom "e,logF a meaning8ul phraseF 'ut !hen "e,is shortene# to Iust ,F the connection 'et!een meaning an#8orm is !ea"ene#. here8oreF !e can not al!ays 8in# a goo# connection 'et!een a !or#Es meaning an#8ormF so !e canEt Iust memorize this connectionK to repro#uce the !or#Es 8orm.

here8ore !e no! "no! 7honetically ntuiti%e


27/36

!hich !ill lo!er the aK in insuranceK 'y 15F ma"ing the !or# loo" li"e

insurance

+8 courseF !e can also encapsulate the style property into a $>> class so that the a'o%e * co#e

can shrin" to something li"e

ins(rs&an classDloeredD#as&an#nce

istorica! >otes

came up !ith this i#ea in &ugust 200? )senet postFthrea#3.

1.1.2.2.2. +rogressive (or' Acuisition $+(A%

n *1#ri%en *2 teaching see >ection 1.1.13F long !or#s are optionally split into small segmentsusually t!o sylla'les long3 an# taught progressi%elyF an# e%en practice# progressi%ely. his lets theuser learn Iust a little 'it each time an# pay more attention to each 'it so that he !oul#nEt Iust learn anincomplete 8orm o8 a !or# as #iscusse# in >ection 1.1.2.1.13. -or e=ampleF !hen

&'()*

$hinese 8or $olora#oK3 8irst appears in a $hinese personEs e' 'ro!serF the computer inserts $oloEa8ter it optionally !ith $oloEs pronunciation3:

&'()* $oloE3

hen &'()* appears 8or the secon# timeF the computer may #eci#e to test the userEs memory a'out$oloE so it replaces &'()* !ith

$oloE )> state3

Bote that a hint such as )> stateK is necessary in or#er to #i88erentiate this $oloE 8rom other !or#s'eginning !ith $olo.

-or the thir# occurrence o8 &'()*F the computer teaches the 8ull 8ormF $olora#oF 'y inserting ita8ter the $hinese occurrence:

&'()* $olora#o3

&t the 8ourth timeF the computer may totally replace &'()* !ith

$olora#o

Bot only the 8oreign language element $olora#o3 can emerge gra#uallyF the original nati%e languageelement &'()*3 can also gra#ually 8a#e outF either %isually or semantically e.g. &'()* +
https://groups.google.com/group/list.linguist/msg/af495996ff06a52f?hl=enhttps://groups.google.com/group/list.linguist/msg/af495996ff06a52f?hl=enhttps://groups.google.com/group/list.linguist/browse_frm/thread/00e521b8e80e8648/af495996ff06a52f?hl=enaf495996ff06a52fhttps://groups.google.com/group/list.linguist/msg/af495996ff06a52f?hl=enhttps://groups.google.com/group/list.linguist/browse_frm/thread/00e521b8e80e8648/af495996ff06a52f?hl=enaf495996ff06a52f


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,-* ./ F !hich means $olora#o W )> state W place name W 3 . his pre%ents the learner8rom su##enly losing the $hinese clueF !hile also engaging him in acti%e recalls o8 the occurrenceEscomplete meaning &'()*3 !ith gra#ually re#uce# clues.

1.1.2.,. +rinciples Learne'

his section #iscusses se%eral principles o8 !or# memorizationK learne# 8rom !or# memorizationmetho#s #iscusse# in pre%ious sectionsF gi%ing us a more 8un#amental un#erstan#ing o8 !hy thesemetho#s !or".

+rincip!e o0 4epetitionuse# in: *1ri%en *2 eaching3: he more times you learn or use a !or#Fthe 'etter you memorize it. his is !hy *1#ri%en *2 teaching teaches an# practices a ne! !or#se%eral times in conte=t 'e8ore consi#ering it as learne# 'y the user.

+rincip!e o0 8egmentationuse# in: 7rogressi%e or# &cuisition3: & %ery long !or# ha# 'etter 'esplit into smaller segments an# taught gra#ually. his is the rationale 8or 7rogressi%e or#

&cuisitionK.

+rincip!e o0 Amp!i0icationuse# in: +rthographically ntuiti%e opronunciation shoul# play an early an# critical role in !or# acuisition. 7honetically ntuiti%e


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spellingF 'ut 7honetically ntuiti%e ections 1.2.2 an# 1.2.( #iscuss t!o such toolsFone 8or ma"ing syntactically %ali# sentencesF the other 8or choosing topically correct !or#s an# larger'uil#ing 'loc"s such as essay templates.

Corrective $riting aidsretroacti%ely e=amines !hat is Iust input 8or possi'le errors an# suggestions.

& spell chec"er is a typical e=ampleF !hich chec"s 8or misspellings in input. $orrecti%e !riting ai#s area much researche# areaF as most natural language analysis techniues can 'e applie# to e=aminesentences 8or in%ali# usagesF an# there are stu#ies on nonnati%e !riting phenomena such as !rongcollocations. here8ore this e'oo" #oes not e=pan# this topic.

1.2.2. &nput!"riven )nta Ai'# $&"A%

&s a nonnati%e


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istorica! >otes

he un#erlying theories o8 this i#ea are !i#ely "no!n in linguistics as case grammar an# 8ramesemantics.

1.2.,. &nput!"riven Ontolog) Ai'# $&"OA%

&s a nonnati%e otes

came up !ith this i#ea in arch 2009 )senet postFthrea#3.

1.,. Foreign Language 8ea'ing Ai's

)nli"e nonnati%e !ritingF nonnati%e rea#ing #oesnEt reuire much help 8rom sophisticate# tools. &learner !ith 'asic


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li"e *1#ri%en *2 teachingF !ith the only #i88erence that the main te=t is in the 8oreign language ratherthan the nati%e language. his ena'les the computer to teach a##itional "no!le#ge such as i#ioms an#grammatical usages that are 'eyon# !or#8or!or# translation. or#speci8ic synta=es as #iscusse# in>ection 1.2.2 nputri%en >ynta= &i#K an# #omainspeci8ic %oca'ularies as #iscusse# in >ection1.2.( nputri%en +ntology &i#K are also goo# 8ee#s.


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or

passe# the !ithhishelp test.

>oF in or#er to translate the original


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a#%ance. -or e=ampleF i8 the computer can positi%ely i#enti8y a sentenceEs su'IectF it can mar"that su'Iect !ith a su'Iect mar"erKF so that the user !ill "no! itEs a su'Iect. &nother e=ampleis a %er'Es transiti%ityF !hich can 'e mar"e# !ith %tK or %iK.

ard-to-disam"iguate s#ntactic 0eaturesare le8t unchange# in the translation result 'ut may'e transcri'e# to the userEs nati%e alpha'et 8or rea#a'ility3. -or e=ampleF the otes

came up !ith this i#ea in arch 2010 )senet posts 1an# 23.

2.2. Foreign Language 9eneration

o! #o !e generate in8ormation in a 8oreign languageF !ithout learning that 8oreign language6achine translation 3 is o8ten the only option. o!e%erF #oesnEt generate pu'licationualitytranslation results. >ection 2.2.1 intro#uces an approach to generating te=t in a 8oreign language inper8ect ualityF !hich reuires that the source te=t 'e !ritten in unam'iguous syntactic structures !ell8orme# synta=3 an# content !or#s 'e #isam'iguate# either automatically or manually.
https://groups.google.com/group/list.linguist/msg/9ffa5ac96b4c3765?hl=enhttps://groups.google.com/group/list.linguist/msg/9ffa5ac96b4c3765?hl=enhttps://groups.google.com/group/list.linguist/msg/acdfb56b0874bd5a?hl=enhttps://groups.google.com/group/list.linguist/msg/9ffa5ac96b4c3765?hl=enhttps://groups.google.com/group/list.linguist/msg/acdfb56b0874bd5a?hl=en


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2.2.1. Foral Language 3achine Translation# $FL3T%

& person not "no!ing a target language can generate in8ormation in that language 'y 8irst !riting hisin8ormation in a 8ormal language L !here syntactic structures are !ritten in an unam'iguous manner8rom the %ery 'eginningF an# content !or#s are 8rom his nati%e %oca'ulary 'ut !ill 'e automatically ormanually #isam'iguate#. he 8ormal language composition !ill then 'e machinetranslate# to %irtuallyany 8oreign language in per8ect uality.

A Quick ,?amp!e

>uppose the userEs nati%e language is ection 1.2.23helps the user use %ali# synta=. -or e=ampleF in !riting the a'o%e 8ormal sentenceF as soon as the userinputs 8o=.KF the synta= ai# !ill sho! actions that a 8o= can ta"eF an# as soon as the user inputsIumpKF the synta= ai# sho!s possi'le roles that can 'e playe# in a Iump e%entF one o8 them 'eingsomething that is Iumpe# o%erF !hich is la'ele# o%erPo'IectK.

Le?ica! (isam"iguation

8 a content !or# in such a 8ormalsynta= sentence is am'iguousF automatic !or# sense #isam'iguation>3 metho#s can calculate the most li"ely sense an# imme#iately in8orm the user o8 this calculate#sense 'y #isplaying a synonym 'elo! the original !or# accor#ing to this sense. he user can manuallyreselect a sense i8 the machinecalculate# sense is !rong. &ll multisense content !or#s are initiallymar"e# as uncon8irme#K e.g. using #otte# un#erlines3F !hich means their machinecalculate# sensesare su'Iect to automatic change i8 later entere# te=t suggests a 'etter interpretation. &n uncon8irme#!or# 'ecomes con8irme# !hen the user corrects the machinecalculate# sense o8 that !or#F or !henthe user hits a special "ey to ma"e all currently uncon8irme# !or#s con8irme#. his process is li"e ho!people input an# con8irm a $hinese string !ith a $hinese input metho#. n a##itionF i8 the computer8eels certain a'out a !or#Es #isam'iguation e.g. the #isam'iguation is 'ase# on a relia'le clue such as

a collocation3F it can automatically ma"e that !or# con8irme#K remo%e its un#erline3.

*achine Trans!ation to >atura! Languages

&8ter all le=ical am'iguity is resol%e# either automatically or manuallyF the computer can procee# tomachinetranslating the 8ormal language composition to any target natural language.

*achine Trans!ation to a 8tandard orm


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he 6ni(ersal Net"oring Language %6NL&7roIect has 'een trying to #o e=actly !hat is #iscusse#a'o%e: machinetranslating a 8ormal language composition to all maIor natural languagesF 'ut it hasnEt'orne 8ruit 8or 20 years. his is 'ecause natural language generation B*D3 8or all maIor languages is a8ormi#a'le engineering challenge. &n alternati%e approach thin" may !or" is to machinetranslate the8ormal language composition to a 8ormal 'ut humanrea#a'le 8orm li"e this:

a fox!"#$% &'o()

*!+,o-.'/ 01. 2o34a56

7

istorica! >otes

here are uite a 8e! attempts at this approach. he most nota'le one is the )B* )ni%ersalBet!or"ing *anguage3 at http://!!!.un#l.org.

in#epen#ently came up !ith this i#ea in 200(F 'y the en# o8 high school.
http://www.undl.org/http://www.undl.org/

Breaking the Language Barrier: A Game-Changing Approach

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