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Addicter
The introductory page on the Addicter project is here.
This page lies in the external name space and is intended for collaboration with people outside of ÚFAL.
Progress, Results and TODO
word alignment
!!! alternative model comparison
| Precision/Recall/F-score: | |||||||||
| Lex | Order | Punct | Miss | ||||||
| meteor | 0.092/0.251/0.135 | 0.047/0.229/'0.078' | 0.248/0.665/'0.361' | 0.020/0.382/'0.038' |
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| ter* | 0.106/0.387/'0.167' | 0.025/0.191/'0.044' | 0.132/0.936/'0.232' | 0.026/0.170/'0.046' |
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| hmm | 0.162/0.426/'0.234' | 0.069/0.309/'0.112' | 0.281/0.793/'0.415' | 0.025/0.400/'0.047' |
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| lcs | 0.168/0.462/'0.247' | 0.000/0.000/'0.000' | 0.293/0.848/'0.435' | 0.026/0.374/'0.049' |
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| gizadiag* | 0.183/0.512/'0.270' | 0.044/0.250/'0.075' | 0.285/0.784/'0.417' | 0.038/0.224/'0.065' |
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| gizainter | 0.170/0.483/'0.252' | 0.049/0.137/'0.072' | 0.284/0.878/'0.429' | 0.029/0.409/'0.054' |
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| berkeley* | 0.200/0.540/'0.291' | 0.050/0.330/'0.087' | 0.292/0.844/'0.434' | 0.039/0.267/'0.068' |
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!!! Explicit wrong lex choice detection
* align input+czeng to reference+czeng and input+czeng to hypotheses+czeng
* extract hypothesis-to-reference alignments from there
| Precision/Recall/F-score: | |||||||||
| Lex | Order | Punct | Miss | ||||||
| czengdiag* | 0.187/0.514/'0.275' | 0.069/0.455/'0.120' | 0.230/0.883/'0.365' | 0.035/0.234/'0.061' |
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| czenginter | 0.197/0.543/'0.290' | 0.108/0.475/'0.176' | 0.233/0.926/'0.372' | 0.032/0.402/'0.060' |
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* TODO: also try domain adaptation for word alignment, EMNLP 2011 paper
!!! alignment combinations via weighed HMM
| Precision/Recall/F-score: | |||||||||
| Lex | Order | Punct | Miss | ||||||
| meteor+hmm | 0.162/0.426/'0.234' | 0.068/0.309/'0.112' | 0.286/0.794/'0.421' | 0.025/0.400/'0.047' |
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| ter+hmm | 0.116/0.402/'0.180' | 0.030/0.184/'0.051' | 0.145/0.912/'0.251' | 0.026/0.181/'0.046' |
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| gizadiag+hmm | 0.186/0.515/'0.273' | 0.040/0.215/'0.067' | 0.297/0.836/'0.438' | 0.039/0.238/'0.067' |
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| gizainter+hmm | 0.194/0.505/'0.281' | 0.062/0.282/'0.101' | 0.299/0.806/'0.436' | 0.033/0.382/'0.061' |
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| berkeley+hmm | 0.203/0.548/'0.297' | 0.049/0.320/'0.085' | 0.290/0.816/'0.428' | 0.041/0.277/'0.071' |
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| czengdiag+hmm | 0.190/0.517/'0.278' | 0.073/0.457/'0.126' | 0.291/0.841/'0.432' | 0.039/0.238/'0.067' |
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| czenginter+hmm | 0.214/0.545/'0.307' | 0.093/0.525/'0.158' | 0.304/0.818/'0.443' | 0.038/0.363/'0.068' |
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| berkeley+czenginter+hmm | 0.219/0.568/'0.316' | 0.070/0.432/'0.120' | 0.298/0.817/'0.436' | 0.048/0.290/'0.082' |
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| berkeley+czenginter+gizainter+hmm | 0.220/0.569/'0.317' | 0.068/0.420/'0.118' | 0.298/0.812/'0.436' | 0.048/0.290/'0.083' |
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| berkeley+czenginter+meteor+hmm | 0.220/0.569/'0.317' | 0.070/0.440/'0.121' | 0.295/0.810/'0.433' | 0.048/0.290/'0.083' |
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!!! TODO
* test alignment with synonym detection (cz_wn required) = separating @@lex@@ and @@disam@@
* order evaluation
a lot of background research
currently finds misplaced items, but their shift distances are off
* to fix – for every misplaced token
if it (and only it) were to be moved in the original permutation, what would be the best place?
evaluate with nr. of intersections
* comb and comment the code
* add help files
* integrate with the rest of Addicter
* learner's corpus
see Anne Lüdelig, TLT9
* adapt to Sara's program
* alternative to reference-based evaluation: “Inconsistencies in Penn parsing”, M. Dickinson