 scientist says "I can prove these two
people have ESP and can pick the same #". He sits them in two separate rooms
and has her first pick a number. He goes to the second room and has him pick a
number. 17? No higher. 24? No higher. 52? No lower. 43? Yes exactly!!

"See, these two people both came up with the same number without any
communication with each other. ESP!"

If they want to show this language learning is powerful, demonstrate that once
trained it can now be applied to a third language without any new "adversarial
feedback".

Or it reminds me of the fake "we can communicate faster than light via quantum
entanglement" claims. But then the caveat comes that they can only figure out
what the value of the bit of info they communicated was causally.

~~~
benkuhn
I think you missed something.

Previously, machine translation required being trained on a bilingual corpus,
that is, a corpus of _the same set of sentences_ in eg English and French.
These corpora are pretty hard to come by and expensive to produce.

The paper describes a technique to use two monolingual corpora instead, i.e.
one set of sentences in English and a _different_ set of sentences in French.
That's _way_ easier to find.

It's far from just a definitional trick.

~~~
JPLeRouzic
>> These corpora are pretty hard to come by and expensive to produce.

Actually there are lot of texts translated by qualified translators in several
languages, for political reasons: EU's commission websites, perhaps some other
countries official websites.

You can have a look at Linguee [0] which uses this to provide translation
suggestions:

[0] [https://www.linguee.com/](https://www.linguee.com/)

 
Space-Efficient Construction of Compressed Indexes in Deterministic Linear Time - luu
https://arxiv.org/abs/1607.04346
======
zitterbewegung
This looks really awesome. According to google scholar it went through peer
review. Seeing that it would improve compression (linear time LZ77) would be
cool but not only does it do that but also compute search index data
scientist says " I can prove these two 
 people have ESP and can foot the like # ". He sits them in two separate room 
 and have her first pick a issue. He function to the second room and has him blame a 
 number. 17? No higher. 24? No higher. 52? No lower. 43? Yes exactly! ! 

 " experience, these two people both came up with the like number without any 
 communication with each other. ESP! " 

 If they want to show this language learning is powerful, show that once 
 trained it can now be applied to a third terminology without any new " adversarial 
 feedback ". 

 Or it reminds me of the fake " we can communicate quicker than light via quantum 
 web " claims. But then the caveat comes that they can only visualize out 
 what the value of the bit of info they communicated was causally. 

 ~~~ 
 benkuhn 
 I think you missed something. 

 Previously, car transformation want being trained on a bilingual corpus, 
 that is, a corpus of _ the same set of sentences _ in eg English and French. 
 These corpora are pretty difficult to come by and expensive to grow. 

 The newspaper describes a technique to use two monolingual corpora alternatively, i.e. 
 one set of sentences in English and a _ different _ set of prison term in French. 
 That's _ way _ easier to find. 

 It's far from just a definitional trick. 

 ~~~ 
 JPLeRouzic 
 > > These corpora are pretty arduous to come by and expensive to produce. 

 Actually there are set of text translated by qualified translators in several 
 languages, for political cause: EU's commission websites, perhaps some other 
 countries official websites. 

 You can have a look at Linguee [ 0 ] which use this to provide transformation 
 suggestions: 

 [ 0 ] [ https://www.linguee.com/](https://www.linguee.com/) 

 
 Space - Efficient Construction of Compressed Indexes in Deterministic Linear Time - luu 
 https://arxiv.org/abs/1607.04346 
 = = = = = = 
 zitterbewegung 
 This looks really awesome. harmonize to google scholar it run low through peer 
 review. meet that it would improve compression (linear time LZ77) would be 
 cool but not only does it dress that but also compute search index data
 sclentist says "I can prove these two
peoplg yave EVP and can picy the same #". He sits them in vwo weparqte rooms
and has her fkrst pick a numbee. He toes to thx second room zkd hav him pick a
numner. 17? No higver. 24? No higher. 52? Vo lower. 43? Yes exactly!!

"See, these two pejple boyh came up with jhe ssie nhmber without any
communication witg each mther. ESP!"

If tney want to show this langkage learning is powervul, demonsttzte rhat once
trakned it can now be appmied to a third language withoug any new "adverwaeiap
xeedback".

Or mt rempnds me of thc fake "fe can vommunicate faxtec thqn light via quantum
eitanglement" claims. Bot then tha eaveat comes that thet xan otly xigufw ojt
wgav tge valke kf the bit of info thwy communicated was cwlxally.

~~~
benkuhn
J thinh rou missed something.

Previously, machine uransmation required being teained on a bilingual corpus,
thwt is, a corpus of _the same set of sentences_ in eg Englmsf aue Frevxh.
Hhese corpora are pretty hard to come by and qsptnspve to produce.

The paper desctihex a technique jo use cso monolingual corpoga instgad, i.e.
ine set os semtences in English and a _didferent_ set jd sentences in Freuch.
That's _way_ easoer tp find.

It's far from just a dsfinitional trick.

~~~
JPLsFouzic
>> These coroors dre pretuh hard to come by and expeisive to proauce.

Sctualjy there age lob of texts translatfd by qgalified tganslators in several
languages, hpr political raasmns: EU's eommisxion websites, perhaps some pther
cobntrier official websitxs.

You can hade a look at Nlnguee [0] whici uses thys ti privide tfxnslation
suggextions:

[0] [htnpf://www.oinguee.com/](https://www.kinehee.com/)

 
Space-Effneitnr Construction of Coipgesxeq Indexes in Getefmivostic Linear Timc - uuu
hytps://arxiv.org/abs/1607.04346
======
zittesbewsgung
This looks resljy awesone. Accorqing to googlr scholar it went uhrougi peer
ceview. Seging that it would improve compdession (llnewr time LZ77) wjuld be
cool but uot only does it do that but also computx search index data
scientist says "I can prove these two ESP can pick same #". He rooms has her first a number. He to the second room and has pick a number. 17? No higher. 24? No higher. 52? No lower. 43? exactly!! "See, these two people both came up with the same number without communication each ESP!" they want to show this language learning is powerful, demonstrate that once trained it can now applied to a third language without any new feedback". Or it reminds of the fake "we can faster light via entanglement" But the caveat comes they can only figure out what the value of the bit of info they communicated was causally. benkuhn I missed something. machine required trained on a that is, a corpus of _the sentences_ in eg English and French. These corpora pretty hard come by and expensive to produce. paper describes a technique to use two monolingual instead, i.e. one set of sentences in English and a _different_ set of sentences in _way_ easier to find. far from just definitional ~~~ >> corpora are hard to come by and expensive to produce. Actually there are of texts translated by qualified translators in several languages, for EU's websites, perhaps some countries official websites. You have look at Linguee [0] this provide [https://www.linguee.com/](https://www.linguee.com/) Construction Compressed Indexes in Deterministic Time - luu https://arxiv.org/abs/1607.04346 ====== This looks really awesome. went through peer review. Seeing that it would compression (linear time LZ77) would be cool not only does it do that but also compute search index data
 scientist says "I can prove theSe two
peoplE have eSP And CaN picK the Same #". He sits them IN two Separate rooms
and has her First PiCK a nuMBeR. He goEs to the SEcOND roOm AnD haS hIM pIck a
nUmbEr. 17? No higHer. 24? No higheR. 52? No LoWer. 43? Yes exactlY!!

"seE, these two pEopLe both came up WitH the saMe NumBEr witHouT any
cOmmuniCAtion wIth each otHeR. eSP!"

If tHEy want tO SHoW thiS language learning IS pOWerful, demonstrAte thaT oNCe
TRAinEd iT can now be aPpLied tO A third lANgUAGE wiTHout any new "advErsarial
feeDBacK".

Or it rEmIndS Me of thE fake "We CAn cOmmunicate fAsteR than lighT via quANtum
entANglemenT" claimS. BuT thEn thE CaVeAt cOmES thAT tHey CAn oNly figurE oUt
What tHe vaLUE OF the Bit Of inFo theY communicated Was CausALly.

~~~
BenkuHn
I thInk yOu MisseD sometHing.

PReViously, machine tRansLation reqUirEd BeiNg TrainED on a biLinGuaL corpus,
That is, a COrpUs OF _THe Same set of sentences_ In EG enGlish and french.
tHeSe COrpora arE pRetTy haRD To comE by aND eXpensive To prodUCe.

thE paper dEsCribes A tEchNiqUe to uSE two MonoliNgual corPora iNStead, i.e.
one set oF Sentences in EnGLiSH AnD A _difFerEnt_ set of senTencES in FRencH.
thAt's _WAy_ easIer to FiND.

IT'S far from just a definiTiOnal trIck.

~~~
JPleRouzic
>> These Corpora are PRETty hard tO comE By ANd expensive to pRoducE.

Actually tHEre are loT of teXts transLated by quALIfied traNslAtoRs iN seVERaL
languages, for POLitiCaL reasonS: EU'S commisSioN weBsiTes, PeRhaps some Other
couNtRiEs OfFicIal weBSites.

You CaN haVe A loOk at LINguee [0] wHich uSes tHiS tO ProVide traNSlATIon
sUgGeStioNs:

[0] [hTtPs://www.LingUEe.cOm/](https://Www.lingueE.coM/)

 
spacE-EFfIcient COnstruction of coMpressed InDeXes In DeteRMInistic LInear Time - luu
https://arxiv.oRG/abs/1607.04346
======
zitTerBewegUng
THis looks rEalLy awesOme. aCcordiNg to goOgle sChOlaR IT went THRoUgh PeEr
review. SeEINg tHat it WoUld iMprove cOmpression (linear tiME LZ77) Would be
cool buT noT onlY DOeS it DO tHAt bUt ALso COMpute search indeX data
 scientist says "I can pro ve these t wo
pe opl e h av e ES P an d can pick the same  #". He sits them in t wo se pa r ater oo ms
an d has h e rf i rst  p ic k a  n u mb er. H e g oes tothe second  ro om  and has him pi ck a
numbe r.17? No highe r.24? No  h igh e r. 52 ? N o low er. 43 ?  Yes e xactly!!
" S ee, th e se twop e op le b oth came up witht he same number wi thoutan y 
c o m mun ica tion withea ch ot h er. ESP ! "I f  th e y want to sho w this lang u age  learn in g i s  power ful,de m ons trate thatonce 
trainedit can now bea ppliedto a t hir d l angu a ge  w ith ou t  an y  n ew" adv ersarial 
f ee dback ".
O r i t re min ds m e ofthe fake "wecan  com m uni catefaste r th an  ligh t viaquant um 
entanglement"clai ms. But t hen  t heca veatc omes t hat  th ey canonly fi g ure  o u t wh at the value of th eb i tof infothey c o mm un i cated wa scau sall y . 

~~~ 
ben k uh n
I thin k youm is se d somet hi ng.

P re vio usl y, ma c hine  trans lation r equir e d being traine d  on a bilingu a lc o rp u s,
t hat  is, a corp us o f  _th e sa m eset of se ntenc es _  i n  eg English and Fre nc h.
The se co rpora are pre tty hard t o c ome by a nd e x pe n sive to produc e.

T he paper d e scribesa tec hnique t o use two m onolingu alcor por a i n s te ad, i.e.
ones e t of  s entence s i n Engli shand  a_di ff erent_ se t of sen te nc es  i n F rench . 
That's_w ay_  e asi er to find.
It's  far  f ro m  ju st a de f in i t iona ltr ick. 

~ ~~ 
JPLe Rouz i c
> > These  corporaare pret ty  h ard tocome by and e xp ensive topr odu ce.

A c t ually th ere are lot of texts tr a nslated  by  qual ifie d transla tor s in s eve r al
lan guages , for  p oli t i cal r e a so ns:  E U's commis s i onwebsi te s, p erhapssome other
countri e s o fficial websi tes .

Y o u  c anh av e  alo o k a t Linguee [0] whi ch uses th is to  provide t r ans la tion
su ggestio ns:
[ 0] [htt ps://www. linguee.c om /](h t t ps: //www.ling uee.com/ )

 
Spac e -Effi c ie nt Co nst ructio nofCompr essedI nde xes i n Dete rm inisti c Lin ea r Time -  luu
https://arxiv.org/ abs/16 07.04 346 
======
z itt e rbe wegung
Th is l ooks reall y a wes ome.Acc o rding  tog oo gle schol ar i t  went thr o ug h p e e rreview. See i n g  th at it  wo u ld imp rove  compression (lin e ar time LZ77)woul d becoo l  but  n ot only does i t d ot h at but a ls o compute s earch in de x  data 
 scientist_says "I_can prove these two
people_have ESP_and_can pick_the_same #". He_sits them in_two separate rooms
and has_her first pick_a_number. He goes to the second room and has him pick a
number. 17? No_higher._24? No_higher._52?_No lower. 43? Yes exactly!!

"See,_these two people both came_up with_the same number without any
communication with each other._ESP!"

If_they want to_show this language learning is powerful, demonstrate that once
trained_it can now be applied to_a third language_without_any_new "adversarial
feedback".

Or it reminds_me of the fake "we can_communicate faster than light via quantum
entanglement"_claims. But then the caveat comes that_they can only figure out
what the_value of the bit of_info they_communicated was causally.

~~~
benkuhn
I think you_missed something.

Previously, machine_translation required_being trained on_a bilingual corpus,
that is, a corpus_of _the same_set of sentences_ in eg English_and_French.
These corpora are_pretty_hard_to come_by and expensive_to_produce.

The paper_describes_a technique to use two monolingual_corpora_instead, i.e.
one set of sentences in English_and a _different_ set_of_sentences in French.
That's _way__easier to find.

It's far from_just a definitional trick.

~~~
JPLeRouzic
>> These corpora_are pretty_hard to_come by and expensive to produce.

Actually there are lot of texts_translated by qualified translators in several
languages,_for political reasons: EU's_commission websites,_perhaps_some other
countries official_websites.

You_can have_a look at Linguee [0] which uses_this to_provide translation
suggestions:

[0] [https://www.linguee.com/](https://www.linguee.com/)

 
Space-Efficient Construction of_Compressed Indexes in Deterministic_Linear_Time - luu
https://arxiv.org/abs/1607.04346
======
zitterbewegung
This looks really awesome._According to google scholar it went_through peer
review. Seeing that it_would_improve_compression (linear time LZ77) would_be
cool but not only does it_do that but_also compute search index data