antot/neural_vs_-phrasebased_smt_eacl17

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.


-------------------
Contents (folders):
-------------------

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





-----------------
Contents (files):
-----------------

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