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README
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README
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This repository contains files related to the publication:
Antonio Toral and Víctor M. Sánchez-Cartagena. A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions. 15th EACL Conference. 2017.
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Contents (folders):
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code/ Code developed (Python):
- Stemmer
- Tokenizer
- Scores by length
data*/ Urls to download the monolingual and parallel training data used
third/ Third party code used
- chrF evaluation metric
- Czech stemmer
- hjerson
- Moses v3 scripts
references/ References of WMT16 in their original format (sgm) and processed:
- tokenised (.tok)
- truecased (.true)
- stemmed (.base). Czech stemming has 2 variants (-aggresive, -light)
systems/ MT outputs of the best submissions at WMT6 in their original format (sgm)
and processed:
- tokenised (.tok)
- truecased (.true)
- stemmed (.base). Czech stemming has 2 variants (-aggresive, -light)
overlaps/ Output of the output similarity experiment (Section 3)
scores_*length/ Outputs of the experiment regarding sentence length (Section 6)
hjerson/ Outputs of the error categories experiment (Section 7) in different formats:
- .cats
- .errs
- .html
- .out (used to report results in the paper, Tables 7 and 8)
- .sents
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Contents (files):
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preprocessing.sh Data preprocessing:
- desgmise
- train monolingual data
- train parallel data
- reference translations and MT outputs
experiments.sh Experiments:
- Output similarity (Section 3). Function overlap_metric
- Fluency (Section 4). VSC TODO
- Reordering (Section 5). VSC TODO
- Sentence length (Section 6). Function scores_by_length
- Error categories (Section 7). Function hjerson
systems_list.txt List and description of the machine translation systems used
in the experiments
russian_stem_fix.txt Instructions to fix the output of the stemmer used for Russian