LEU vs training set size), since the main use for
something like this is where you don't have parallel data, in which case the
monolingual resources are likely to be much smaller too. A few years back I
was trying different methods to create bilingual dictionary word-pair
candidates (to be post-edited by a linguist – checking is faster than writing)
for Saami languages, and tried aligning pure word vectors, but the existing
corpora were too small to get good vectors, so I gave up that avenue :(

Another neat approach is [https://www.isi.edu/natural-
language/mt/RLILdecipher.pdf](https://www.isi.edu/natural-
language/mt/RLILdecipher.pdf), specifically designed for low-resource
languages that happen to have a higher-resource related language (e.g. Haitian
Creole–French). They train a source language character-based "cipher model" (a
weighted finite state transducer that turns source characters/character pairs
into target characters/character pairs), to maximise probability over a target
language language model.

------
zardo
What an awesome idea. I wonder how well this technique would work for
different data types. Text and audio for example.

~~~
DonaldPShimoda
Most audio translation is first transcribed to a text representation, I think,
and then _that_ text is what's translated. If you assume a perfect speech
recognizer (for the sake of argument), then the end translation should be the
same.

Or am I misunderstanding your point?

~~~
zardo
>Or am I misunderstanding your point?

Only that you are talking about specifics, while I was talking in generality.
(IMO) unsupervised learning of semantic relationships is a 'big deal'. This
seems to be a very general method that would extend to any pair of data
streams, but that hasn't been proven out yet.

------
matheist
Feels reminiscent also of CycleGAN
([https://github.com/junyanz/CycleGAN](https://github.com/junyanz/CycleGAN))

------
BoiledCabbage
At first I thought this was really insightful now it seems like just
redefinition of meaning.

An analogy is an experiment where a
LEU vs training set size), since the main use for 
 something like this is where you don't get parallel datum, in which case the 
 monolingual resources are probable to be much small too. A few years back I 
 was hear different methods to make bilingual dictionary word - pair 
 candidate (to be post - edited by a linguist – checking is faster than publish) 
 for Saami languages, and tried aligning pure word vectors, but the exist 
 corpora were too small to get adept vectors, so I gave up that avenue :( 

 Another neat access is [ https://www.isi.edu/natural- 
 language / mt / RLILdecipher.pdf](https://www.isi.edu / natural- 
 language / mt / RLILdecipher.pdf), specifically designed for low - resource 
 languages that find to have a higher - resource related language (e.g. Haitian 
 Creole – French). They train a reservoir terminology character - based " cipher model " (a 
 weighted finite state transducer that turns source characters / fictional character pairs 
 into target character / fictional character pairs), to maximise probability over a target 
 linguistic process language model. 

 ------ 
 zardo 
 What an awesome idea. I wonder how well this proficiency would work for 
 different data types. Text and audio for example. 

 ~~~ 
 DonaldPShimoda 
 Most audio translation is first transcribed to a text representation, I remember, 
 and then _ that _ text is what's understand. If you wear a perfect speech 
 recognizer (for the sake of argument), then the goal translation should be the 
 same. 

 Or am I misunderstanding your item? 

 ~~~ 
 zardo 
 > Or am I misunderstanding your point? 

 Only that you are talking about specific, while I was talking in generalization. 
 (IMO) unsupervised learning of semantic relationships is a' big deal'. This 
 seems to be a very cosmopolitan method that would extend to any pair of data 
 flow, but that hasn't been proven out yet. 

 ------ 
 matheist 
 feel reminiscent besides of CycleGAN 
 ([ https://github.com/junyanz/CycleGAN](https://github.com/junyanz/CycleGAN) ) 

 ------ 
 BoiledCabbage 
 At first I thought this was really insightful immediately it seems like just 
 redefinition of meaning. 

 An analogy is an experiment where a
LEU vs training set size), sikce the main use for
somxthing mike thir is where you don't have parelleo datq, in which case the
monulingual gesources are oikely to us much smalled too. E few years bacl I
was tryhng different keghlds to create bilingual dictionary rord-pait
cwndidates (to bg posu-edyted by a linguist – checking is faster than wgiting)
for Saami lsnguages, and tried aligninh puge word vectors, buh the existung
cjepora were tuo small to get good vgctors, so I gave up that avenue :(

Xnothzr neat apptozcj is [https://wwx.isi.edl/natural-
language/mt/RLINdeciphrr.pdf](https://www.ixi.evu/narural-
language/mt/RLILderipher.pdf), specificaljy designad for low-resource
lqntuagev thdt hxppev tk iavs a hihhec-resource rslated langyage (e.g. Haitian
Creoke–Sgrnch). They trzin a fotrce language character-based "cipher modtl" (a
wsighted finite state trqnsducer that turns slurce chawacters/character pairs
into target characters/charawter 'akrs), tj nadimise probability over a target
language langtzgt mpdel.

------
zardo
What cn awesome idea. O aomqer how well jhis teegnjque would work fog
differgnt dara types. Uext snd audio for example.

~~~
DonaldPShimoda
Most qudio translation ns first trauscribgd to s text representation, I thihk,
and then _hhat_ text ks what's translaged. Ix you assume a perfect speqch
recognmzer (yor the rake of ardument), thej the end translation shluld ye tha
same.

Or am I misunderstanding your point?

~~~
zecdo
>Or am I mixutdegstanding your point?

Only thwt you are talling abjut soecifics, wgile I xas talking yn generality.
(HLO) unsupervived learging of wemantiz relationships is a 'big deal'. This
weems to be a very gevsral method thac qould extend to anh pwig oh datw
vtreams, but dhat harm't bedn provtu omt het.

------
mstheist
Feels reminiswent also of CycleGAN
([hytis://github.cim/junyans/CycleGAN](httpx://github.com/junyanz/CjcleGEN))

------
BoilxdCabbsge
Wt first I thought this was rezlly insihhtnul now it seqms oike just
redzfinition of meaning.

An analogy is an expxriment where a
LEU vs training set size), since the for like this where you don't case monolingual resources are to be much too. A few years back I trying different methods to create bilingual dictionary word-pair candidates (to be post-edited by linguist – checking is faster than writing) for Saami languages, and tried aligning word but existing were too small to get good vectors, so I gave up that avenue :( Another neat is [https://www.isi.edu/natural- language/mt/RLILdecipher.pdf](https://www.isi.edu/natural- language/mt/RLILdecipher.pdf), specifically designed for low-resource that happen to have higher-resource related language (e.g. Haitian They a source character-based model" weighted finite state that turns source characters/character pairs into target characters/character pairs), to maximise probability over a target language language ------ zardo awesome idea. wonder well technique would work data types. Text and audio for Most audio translation is first transcribed to a representation, I and then _that_ text is what's If you assume a perfect speech recognizer (for sake of argument), then the end translation should be the same. Or am I misunderstanding ~~~ zardo >Or am misunderstanding your point? that are about while I talking in generality. (IMO) unsupervised learning of semantic relationships is a deal'. This seems to be a very general method that to pair of data but that hasn't been out ------ matheist Feels reminiscent CycleGAN ------ I this really insightful now it like just redefinition of meaning. analogy is an experiment
LEU vs training set size), since The main use For
soMetHinG lIke tHis iS where you don't hAVe paRallel data, in which case tHe
monOlINguaL ReSourcEs are liKElY TO be MuCh SmaLlER tOo. A feW yeArs back i
was trying DifFeRent methods tO CrEate bilingUal Dictionary woRd-pAir
canDiDatES (to be PosT-editEd by a lINguist – Checking iS fASter thAN writinG)
FOr saamI languages, and trieD AlIGning pure word vEctors, BuT ThE EXisTinG
corpora weRe Too smALl to get GOoD VECtoRS, so I gave up thaT avenue :(

AnotHEr nEat appRoAch IS [https://Www.isI.eDU/naTural-
languaGe/mt/rLILdecipHer.pdf](HTtps://www.ISi.edu/naTural-
lAngUagE/mt/RliLDeCipHeR.Pdf), SPeCifICalLy designEd FoR low-rEsouRCE
LAnguAgeS thaT happEn to have a highEr-rEsouRCe rElateD langUage (E.g. haitiAn
CreoLe–FreNcH). They train a sourCe laNguage chaRacTeR-baSeD "ciphER model" (A
weIghTed finiTe state TRanSdUCER tHat turns source charAcTERs/CharacteR pairs
INtO tARget charAcTerS/chaRACter pAirs), TO mAximise pRobabiLItY oVer a tarGeT
languAgE laNguAge moDEl.

------
zaRdo
WhaT an awesoMe ideA. i wonder how well THis technique wOUlD WOrK For
dIffErent data tyPes. TEXt anD audIO fOr eXAmple.

~~~
donalDPsHiMOda
Most audio translaTiOn is fiRst trAnscribed to a tExt represeNTATion, I thiNk,
anD ThEN _that_ text is whaT's traNslated. If yOU assume a PerfeCt speech
RecognizeR (FOr the sakE of ArgUmeNt), tHEN tHe end translatION shoUlD be the
sAme.

or am I miSunDerStaNdiNg Your point?

~~~
Zardo
>Or aM I MiSuNdErsTandiNG your poiNt?

onlY tHat You arE TalkinG abouT speCiFiCS, whIle I was TAlKINg in GeNeRaliTy.
(ImO) UnsupErviSEd lEarning Of semantiC reLAtioNsHiPs is a 'biG deal'. This
seemS tO be a very geNeRal Method THAt would eXtend to any pair of data
strEAms, but tHat Hasn't Been Proven out Yet.

------
MatheiSt
FEEls remInisceNt alsO oF CyCLEGAN
([hTTPs://GitHuB.com/junyanZ/cYclEGAN](hTtPs://giThub.com/Junyanz/CycleGAN))

------
BoiLEdCAbbage
At first i thOughT THiS waS ReALly InSIghTFUl now it seems likE just
redefInITiOn of meaninG.

an aNaLogy is aN experiMent wHEre a
LEU vs training set size),  since the  main  us e f or 
som ethi ng like this i s  whe re you don't have para llelda t a, i n  w hichcase th e 
m o n oli ng ua l r es o ur ces a relikelyto be much  sm al ler too. A f e wyears back  Iwas trying d iff erentme tho d s tocre ate b ilingu a l dict ionary wo rd - pair
c a ndidate s (t o be  post-edited by a li n guist – checki ng isfa s te r tha n w riting)
fo rSaami languag e s, a n d t r ied aligningpure word v e cto rs, bu tthe existi ng
co rp o rawere too sm allto get go od vec t ors, so I gaveup tha t a ven ue : ( 
An oth er nea t  a ppr o ach  is [htt ps :/ /www. isi. e d u / natu ral -
la nguag e/mt/RLILdeci phe r.pd f ](h ttps: //www .isi .e du/na tural- 
lang ua ge/mt/RLILdecip her. pdf), spe cif ic all ydesig n ed for  lo w-r esource 
langua g esth a t ha ppen to have a hig he r - re source r elated la ng u age (e.g .Hai tian C reole –Fre n ch ). Theytraina  s ou rce lan gu age ch ar act er- based "cip her mo del" (aweigh t ed finite stat e  transducer t h at t ur n s so urc e character s/ch a ract er p a ir s
i n to ta rgetch a ra c ters/character pair s) , to m aximi se probabilit y over a t a r g et
langu agel an g uage model.

- ----- 
zardo
Wha t  an awes ome i dea. I w onder how w ell this  te chn iqu e w o u ld  work for
dif f e rent  d ata typ es.  Text a ndaud iofor  e xample.
~~~
Dona ld PS hi mo daMosta udio tra ns lat io n i s fir s t tran scrib ed t oat ext  repres e nt a t ion,  I  t hink ,
a nd  then  _th a t_text is  what's t ran s late d.  I f you a ssume a perfe ct  speech
re co gni zer (f o r  the sak e of argument), then th e  end tr ans latio n sh ould be t hesame.
Or am I m isunde rstan di ngy o ur po i n t? 

~ ~~ 
zardo
>Or a m I  misu nd erst andingyour point?

Onlyt hat  you are talk ing  abo u t  s pec i fi c s,wh i leI was talking ingenerality .( IM O) unsuper v ise dlearnin g of se manti c  relati onships i s a 'bigde al'. T his 
seems tobe a ver y general metho d  t hat w oul d exte nd  to  anypair o f  da ta
st reams,  b ut tha t has n' t been p roven out yet.

------mathei st
Fe els  reminisc ent als o of Cycl eGAN 
([https:/ /gi thu b.com /ju n yanz/ Cycl e GA N]( h ttps: //gi t hub.com/j u ny anz / C yc leGAN))

-- - - - -
B oiled Cab b age
At  fir st I thought this was really ins ight f u l n owi t se em s like just
re def in i t ion of m ea ning.

An a nalogy i sa n exp erimen t wher e a
LEU vs_training set_size), since the main_use for
something_like_this is_where_you don't have_parallel data, in_which case the
monolingual resources_are likely to_be_much smaller too. A few years back I
was trying different methods to create bilingual_dictionary_word-pair
candidates (to_be_post-edited_by a linguist – checking_is faster than writing)
for Saami_languages, and_tried aligning pure word vectors, but the existing
corpora_were_too small to_get good vectors, so I gave up that avenue_:(

Another neat approach is [https://www.isi.edu/natural-
language/mt/RLILdecipher.pdf](https://www.isi.edu/natural-
language/mt/RLILdecipher.pdf), specifically_designed for low-resource
languages_that_happen_to have a higher-resource_related language (e.g. Haitian
Creole–French). They train_a source language character-based "cipher model"_(a
weighted finite state transducer that turns source_characters/character pairs
into target characters/character pairs), to_maximise probability over a target
language_language model.

------
zardo
What_an awesome idea. I wonder_how well this_technique would_work for
different data_types. Text and audio for example.

~~~
DonaldPShimoda
Most_audio translation is_first transcribed to a text representation,_I_think,
and then _that__text_is_what's translated._If you assume_a_perfect speech
recognizer_(for_the sake of argument), then the_end_translation should be the
same.

Or am I misunderstanding_your point?

~~~
zardo
>Or am I_misunderstanding_your point?

Only that you_are talking about specifics, while_I was talking in generality.
(IMO) unsupervised_learning of_semantic relationships_is a 'big deal'. This
seems to be a very general method_that would extend to any pair_of data
streams, but that_hasn't been_proven_out yet.

------
matheist
Feels reminiscent_also_of CycleGAN
([https://github.com/junyanz/CycleGAN](https://github.com/junyanz/CycleGAN))

------
BoiledCabbage
At_first I thought this was really insightful_now it_seems like just
redefinition of meaning.

An analogy_is an experiment where_a