Difference between revisions of "Kaingang and Portuguese/Structural Transfer"

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'''Tagger output:'''  
 
'''Tagger output:'''  
 +
“^Inh/inh<prn><pes>/inh<prn><p1><sg>$ ^kã’u/kã’u<v><tv>$ ^ã/ã<prn>/ã<prn><p2><sg>$^,/,<cm>$” ^he/he<v><tv>$ ^tóg/tóg<su>$^./.<sent>$ “^Inh/inh<prn><pes>/inh<prn><p1><sg>$ ^mỹ/mỹ<cir>$ ^ã/ã<prn>/ã<prn><p2><sg>$ ^tóg/tóg<su>$ ^tỹ/tỹ<cir>$^,/,<cm>$ ^ũ/ũ<prn>$ ^nĩ/nĩ<a>/nĩ<n>/nĩ<v><iv><sg>$ ^ven/ven<v><tv><sg>$ ^nĩ/nĩ<a>/nĩ<n>/nĩ<v><iv><sg>$ ^vẽ/vẽ<a>$^,/,<cm>$ ^hã/hã<o>$ ^ra/ra<cir>$ ^ã/ã<prn>/ã<prn><p2><sg>$ ^tóg/tóg<su>$ ^tỹ/tỹ<cir>$^,/,<cm>$ ^ã/ã<prn>/ã<prn><p2><sg>$ ^hã/hã<o>$ ^nĩ/nĩ<a>/nĩ<n>/nĩ<v><iv><sg>$”^,/,<cm>$ ^he/he<v><tv>$ ^tóg/tóg<su>$^./.<sent>$^./.<sent>$
 +
  
 
'''Biltrans output:'''  
 
'''Biltrans output:'''  

Revision as of 15:15, 19 April 2019

Pre-evaluation

Statistics about input files
-------------------------------------------------------
Number of words in reference: 63
Number of words in test: 63 
Number of unknown words (marked with a star) in test: 26
Percentage of unknown words: 41.27 %

Results when removing unknown-word marks (stars)
-------------------------------------------------------
Edit distance: 38
Word error rate (WER): 60.32 %
Number of position-independent correct words: 25
Position-independent word error rate (PER): 60.32 %

Results when unknown-word marks (stars) are not removed
-------------------------------------------------------
Edit distance: 63
Word Error Rate (WER): 100.00 %
Number of position-independent correct words: 0
Position-independent word error rate (PER): 100.00 % 
Statistics about the translation of unknown words
-------------------------------------------------------
Number of unknown words which were free rides: 25
Percentage of unknown words that were free rides: 96.15 %

  • WER: 60.32%
  • PER: 60.32%
  • Coverage:
$ aq-covtest ling073-kgp-por-corpus/kgp.tests.txt ling073-kgp-por/kgp-por.automorf.bin
Number of tokenised words in the corpus: 82
Coverage: 64.63%
Top unknown words in the corpus:
3	 vỹ
2	 fi
1	 kafã
1	 Nũgnũj
1	 kur
1	 ẽgno
1	 tũg
1	 São
1	 Pau
1	 o
1	 rã
1	 jur
1	 tá
1	 Téj
1	 ki
1	 panh
1	 kãfór
1	 kyrũ
1	 jãmré
1	 ũn
Translation time: 0.0028295516967773438 seconds

Example for implementation

Sentence:

“Inh kã’u ã,” he tóg. “Inh mỹ ã tóg tỹ, ũ nĩ ven nĩ vẽ, hã ra ã tóg tỹ, ã hã nĩ”, he tóg.

Portuguese Translation:

“Você me assustou, pensei que você era um outro mas é você mesmo”, ele falou para mim.

Tagger output:

“^Inh/inh<prn><pes>/inh<prn><p1><sg>$ ^kã’u/kã’u<v><tv>$ ^ã/ã<prn>/ã<prn><p2><sg>$^,/,<cm>$” ^he/he<v><tv>$ ^tóg/tóg<su>$^./.<sent>$ “^Inh/inh<prn><pes>/inh<prn><p1><sg>$ ^mỹ/mỹ<cir>$ ^ã/ã<prn>/ã<prn><p2><sg>$ ^tóg/tóg<su>$ ^tỹ/tỹ<cir>$^,/,<cm>$ ^ũ/ũ<prn>$ ^nĩ/nĩ<a>/nĩ<n>/nĩ<v><iv><sg>$ ^ven/ven<v><tv><sg>$ ^nĩ/nĩ<a>/nĩ<n>/nĩ<v><iv><sg>$ ^vẽ/vẽ<a>$^,/,<cm>$ ^hã/hã<o>$ ^ra/ra<cir>$ ^ã/ã<prn>/ã<prn><p2><sg>$ ^tóg/tóg<su>$ ^tỹ/tỹ<cir>$^,/,<cm>$ ^ã/ã<prn>/ã<prn><p2><sg>$ ^hã/hã<o>$ ^nĩ/nĩ<a>/nĩ<n>/nĩ<v><iv><sg>$”^,/,<cm>$ ^he/he<v><tv>$ ^tóg/tóg<su>$^./.<sent>$^./.<sent>$


Biltrans output:

“^Inh<prn><pes>/Eu<prn><tn><pes>/Meu<prn><tn><pes>/De mim<prn><tn><pes>$ ^kã’u<v><tv>/assustar<vblex>$ ^ã<prn>/teu<prn><tn>/seu<prn><tn>/você<prn><tn>/tu<prn><tn><p2><mf><sg>$^,<cm>/,<cm>$” ^he<v><tv>/dizer<vblex>$ ^tóg<su>/ele<prn><tn>$^.<sent>/.<sent>$ “^Inh<prn><pes>/Eu<prn><tn><pes>/Meu<prn><tn><pes>/De mim<prn><tn><pes>$ ^mỹ<cir>/para<pr>$ ^ã<prn>/teu<prn><tn>/seu<prn><tn>/você<prn><tn>/tu<prn><tn><p2><mf><sg>$ ^tóg<su>/ele<prn><tn>$ ^tỹ<cir>/por<pr>/com<pr>$^,<cm>/,<cm>$ ^ũ<prn>/alguém<prn><tn>$ ^nĩ<a>/no momento<adv>$ ^ven<v><tv><sg>/mostrar<vblex>$ ^nĩ<a>/no momento<adv>$ ^vẽ<a>/é<vblex>/era<vblex>/ser<vblex>$^,<cm>/,<cm>$ ^hã<o>/igual<adj>/parecido<adj>$ ^ra<cir>/para<pr>/apesar do<cnjadv>$ ^ã<prn>/teu<prn><tn>/seu<prn><tn>/você<prn><tn>/tu<prn><tn><p2><mf><sg>$ ^tóg<su>/ele<prn><tn>$ ^tỹ<cir>/por<pr>/com<pr>$^,<cm>/,<cm>$ ^ã<prn>/teu<prn><tn>/seu<prn><tn>/você<prn><tn>/tu<prn><tn><p2><mf><sg>$ ^hã<o>/igual<adj>/parecido<adj>$ ^nĩ<a>/no momento<adv>$”^,<cm>/,<cm>$ ^he<v><tv>/dizer<vblex>$ ^tóg<su>/ele<prn><tn>$^.<sent>/.<sent>$^.<sent>/.<sent>

Chunker output:

apertium-transfer: Rule 1 .<sent>/.<sent>
apertium-transfer: Rule 2 mỹ<cir>/para<pr>
apertium-transfer: Rule 3 ã<prn>/tu<prn><tn><p2><mf><sg> tóg<su>/ele<prn><tn>
apertium-transfer: Rule 2 tỹ<cir>/por<pr>/com<pr>
apertium-transfer: Rule 2 ra<cir>/para<pr>/apesar do<cnjadv>
apertium-transfer: Rule 3 ã<prn>/tu<prn><tn><p2><mf><sg> tóg<su>/ele<prn><tn>
apertium-transfer: Rule 2 tỹ<cir>/por<pr>/com<pr>
apertium-transfer: Rule 1 .<sent>/.<sent>
apertium-transfer: Rule 1 .<sent>/.<sent>
“^default<default>{^Eu<prn><tn><pes>$}$ ^default<default>{^assustar<vblex>$}$ ^default<default>{^tu<prn><tn><p2><mf><sg>$}$^default<default>{^,<cm>$}$” ^default<default>{^dizer<vblex>$}$ ^default<default>{^ele<prn><tn>$}$^sent<SENT>{^.<sent>$}$ “^default<default>{^Eu<prn><tn><pes>$}$ ^pr<SP>{^para<pr>$}$ ^prn<SN><CD>{^tu<prn><tn><p2><mf><sg><2>$}$ ^pr<SP>{^por<pr>$}$^default<default>{^,<cm>$}$ ^default<default>{^alguém<prn><tn>$}$ ^default<default>{^no momento<adv>$}$ ^default<default>{^mostrar<vblex>$}$ ^default<default>{^no momento<adv>$}$ ^default<default>{^é<vblex>$}$^default<default>{^,<cm>$}$ ^default<default>{^igual<adj>$}$ ^pr<SP>{^para<pr>$}$ ^prn<SN><CD>{^tu<prn><tn><p2><mf><sg><2>$}$ ^pr<SP>{^por<pr>$}$^default<default>{^,<cm>$}$ ^default<default>{^tu<prn><tn><p2><mf><sg>$}$ ^default<default>{^igual<adj>$}$ ^default<default>{^no momento<adv>$}$”^default<default>{^,<cm>$}$ ^default<default>{^dizer<vblex>$}$ ^default<default>{^ele<prn><tn>$}$^sent<SENT>{^.<sent>$}$^sent<SENT>{^.<sent>$}$

Interchunk output:

apertium-interchunk: Rule 1 prn<SN><CD>{^tu<prn><tn><p2><mf><sg><2>$}
apertium-interchunk: Rule 2 prn<SN><CD>{^tu<prn><tn><p2><mf><sg><2>$} pr<SP>{^por<pr>$}
apertium-interchunk: Rule 1 prn<SN><CD>{^tu<prn><tn><p2><mf><sg><2>$}
apertium-interchunk: Rule 2 prn<SN><CD>{^tu<prn><tn><p2><mf><sg><2>$} pr<SP>{^por<pr>$}
“^default<default>{^Eu<prn><tn><pes>$}$ ^default<default>{^assustar<vblex>$}$ ^default<default>{^tu<prn><tn><p2><mf><sg>$}$^default<default>{^,<cm>$}$” ^default<default>{^dizer<vblex>$}$ ^default<default>{^ele<prn><tn>$}$^sent<SENT>{^.<sent>$}$ “^default<default>{^Eu<prn><tn><pes>$}$ ^pr<SP>{^para<pr>$}$ ^pr<SP>{^por<pr>$}$ ^prn<SN><obj>{^tu<prn><tn><p2><mf><sg><2>$}$^default<default>{^,<cm>$}$ ^default<default>{^alguém<prn><tn>$}$ ^default<default>{^no momento<adv>$}$ ^default<default>{^mostrar<vblex>$}$ ^default<default>{^no momento<adv>$}$ ^default<default>{^é<vblex>$}$^default<default>{^,<cm>$}$ ^default<default>{^igual<adj>$}$ ^pr<SP>{^para<pr>$}$ ^pr<SP>{^por<pr>$}$ ^prn<SN><obj>{^tu<prn><tn><p2><mf><sg><2>$}$^default<default>{^,<cm>$}$ ^default<default>{^tu<prn><tn><p2><mf><sg>$}$ ^default<default>{^igual<adj>$}$ ^default<default>{^no momento<adv>$}$”^default<default>{^,<cm>$}$ ^default<default>{^dizer<vblex>$}$ ^default<default>{^ele<prn><tn>$}$^sent<SENT>{^.<sent>$}$^sent<SENT>{^.<sent>$}$

Postchunk output:

“^Eu<prn><tn><pes>$ ^assustar<vblex>$ ^tu<prn><tn><p2><mf><sg>$^,<cm>$” ^dizer<vblex>$ ^ele<prn><tn>$^.<sent>$ “^Eu<prn><tn><pes>$ ^para<pr>$ ^por<pr>$ ^tu<prn><tn><p2><mf><sg><obj>$^,<cm>$ ^alguém<prn><tn>$ ^no momento<adv>$ ^mostrar<vblex>$ ^no momento<adv>$ ^é<vblex>$^,<cm>$ ^igual<adj>$ ^para<pr>$ ^por<pr>$ ^tu<prn><tn><p2><mf><sg><obj>$^,<cm>$ ^tu<prn><tn><p2><mf><sg>$ ^igual<adj>$ ^no momento<adv>$”^,<cm>$ ^dizer<vblex>$ ^ele<prn><tn>$^.<sent>$^.<sent>$


kgp-por output:

“#Eu #assustar #tu,” #dizer #ele. “#Eu para por ti, #alguém #no momento #mostrar #no momento #é, #igual para por ti, #tu #igual #no momento”, #dizer #ele.

Post-evaluation

$ apertium-eval-translator -r ../tools/kgp.tests.txt -t ../ling073-kgp-por/kgp-por.tests.txt
Test file: '../ling073-kgp-por/kgp-por.tests.txt'
Reference file '../tools/kgp.tests.txt'

Statistics about input files
-------------------------------------------------------
Number of words in reference: 63
Number of words in test: 73
Number of unknown words (marked with a star) in test: 
Percentage of unknown words: 0.00 %

Results when removing unknown-word marks (stars)
-------------------------------------------------------
Edit distance: 72
Word error rate (WER): 114.29 %
Number of position-independent correct words: 1
Position-independent word error rate (PER): 114.29 %

Results when unknown-word marks (stars) are not removed
-------------------------------------------------------
Edit distance: 72
Word Error Rate (WER): 114.29 %
Number of position-independent correct words: 1
Position-independent word error rate (PER): 114.29 %

Statistics about the translation of unknown words
-------------------------------------------------------
Number of unknown words which were free rides: 0
Percentage of unknown words that were free rides: 0%
  • WER: 114.29 % ???
  • PER: 114.29 % ???
  • Coverage:
$ aq-covtest ling073-kgp-por-corpus/kgp.tests.txt ling073-kgp-por/kgp-por.automorf.bin
Number of tokenised words in the corpus: 80
Coverage: 100.00%
Top unknown words in the corpus:
Translation time: 0.002888202667236328 seconds