Data & Software Releases

As part of GoURMET’s efforts to increase resources and tools available
for low-resource machine translation, we have released many of the
corpora and software created during the project. These corpora are also
available at OPUS (http://opus.nlpl.eu/GoURMET.php).

Table of Contents

    Corpora

    English–Swahili parallel corpus
    http://data.statmt.org/gourmet/corpora/GoURMET-crawled.en-sw.zip

    English–Turkish parallel corpus
    http://data.statmt.org/gourmet/corpora/GoURMET-crawled.en-tr.zip

    English–Amharic parallel corpus and Amharic monolingual corpus
    http://data.statmt.org/gourmet/corpora/GoURMET-crawled.en-am.zip

    English–Kyrgyz parallel corpus and Kygryz monolingual corpus
    http://data.statmt.org/gourmet/corpora/GoURMET-crawled.en-ky.zip

    Kyrgyz–Russian parallel corpus
    http://data.statmt.org/gourmet/corpora/GoURMET-crawled.ky-ru.zip

    PMIndia – Parallel corpus of languages of India
    http://data.statmt.org/pmindia/

    English–Serbian parallel corpus
    http://data.statmt.org/gourmet/corpora/GoURMET-crawled.sr-en.zip

    English–Serbo-Croatian parallel corpus
    http://data.statmt.org/gourmet/corpora/GoURMET-crawled.hbs-en.zip

    Monolingual News Crawl
    http://data.statmt.org/news-crawl

    OPUS-100 corpus
    (An English-centric multilingual corpus covering 100 languages sampled from OPUS. All training pairs include English on either the source or target side.)
    https://github.com/EdinburghNLP/opus-100-corpus

    Translation Models

    The repository currently covers seven languages: Bulgarian, Turkish, Swahili, Kyrgyz, Serbian, Amharic and Gujarati
    http://data.statmt.org/gourmet/models/

    Amharic-English
    http://data.statmt.org/gourmet/models/am-en/20200630/mt-engine-am-en.tgz

    Bulgarian-English
    http://data.statmt.org/gourmet/models/bg-en/

    English-Bulgarian
    http://data.statmt.org/gourmet/models/en-bg/

    Gujarati-English
    http://data.statmt.org/gourmet/models/gu-en/20190628/

    English-Gujarati
    http://data.statmt.org/gourmet/models/en-gu/

    Serbian-English
    http://data.statmt.org/gourmet/models/sr-en/20200411/

    English-Serbian (Cyr.)
    http://data.statmt.org/gourmet/models/en-sr.cyr/20200411/

    English-Serbian (Lat.)
    http://data.statmt.org/gourmet/models/en-sr.lat/20200411/

    Turkish-English
    http://data.statmt.org/gourmet/models/tr-en/20200630/mt-engine-tr-en.tgz

    English-Turkish
    http://data.statmt.org/gourmet/models/en-tr/

    Various Docker Modules
    http://data.statmt.org/gourmet/models/docker/

    Software

    mBART pretraining of Marian models
    https://github.com/transducens/smart-segmentation

    Morphological segmentation using Apertium resources
    https://github.com/transducens/smart-segmentation

    Hierarchical decoding (word RNN and decoding of words character by character)
    https://github.com/d-ataman/Char-NMT

    Latent modelling of morphology in NMT
    https://github.com/d-ataman/lmm

    Neural n-to-m alignment models
    https://github.com/Roxot/m-to-n-alignments

    Deep latent variable models for language modelling
    https://github.com/tom-pelsmaeker/deep-generative-lm

    Interpretable text classifiers with sparse relaxations to discrete random variables
    https://github.com/bastings/interpretable_predictions

    Training data for document-level machine translation
    https://github.com/radidd/Doc-substructure-NMT

    Contrastive test sets for document-level machine translation
    https://github.com/rbawden/Large-contrastive-pronoun-testset-EN-FR

    Auto-encoding variational neural machine translation
    https://github.com/Roxot/AEVNMT.pt

    Bayesian data analysis of NMT models
    https://github.com/probabll/bda-nmt

    LASER train (language-agnostic sentence embeddings): It reproduces the architecture described by Artetxe and Schwenk (2018, 2019) to train language-agnostic sentence embeddings.
    https://github.com/transducens/LASERtrain

    LinguaCrawl: It is used to crawl top-level domains. It has been completely developed within the GoURMET project and is compatible with Bitextor, so the data crawled with it can be processed with Bitextor.
    https://github.com/transducens/linguacrawl/

    WMT19 Gujarati system models and scripts
    http://data.statmt.org/wmt19_systems/

    Tool for fusing, extending and using language representations
    https://github.com/aoncevay/multiview-langrep

    Code for the improving massively multilingual NMT work
    https://github.com/bzhangGo/zero

    Code for the language model prior work
    https://github.com/cbaziotis/lm-prior-for-nmt

    Code for the auto-encoding variational NMT work
    https://github.com/Roxot/AEVNMT

    Bitextor: It is used to identify, align and clean parallel data by crawling web domains specified by the user.
    https://github.com/bitextor/bitextor

    It is developed in tight coordination with the Paracrawl project. Most contributions related to GoURMET focus on the addition of components that allow to improve the performance of the tool for under-resourced languages.

    Bicleaner: It is used to filter noisy segment pairs in parallel corpora.
    https://github.com/bitextor/bicleaner

    We have contributed to the master branch and we are also working on another branch (https://github.com/bitextor/bicleaner/tree/bicleaner-0.14-NAACL20) that will eventually be merged with the master one.

    Constrained optimisation for deep generative models in torch
    https://github.com/EelcovdW/pytorch-constrained-opt.git

    Probability distributions for torch including sparse relaxations to discrete random variables
    https://github.com/probabll/dists.pt

    Probabilistic modules for torch
    https://github.com/probabll/dgm.pt

    BayerSeq. Software package that implements the variational autoencoder models of sentences that we used for data augmentation.
    https://github.com/probabll/dgm.pt

    mtl-da Scripts for training machine translation systems using different data augmentation techniques in the target language.
    https://github.com/vitaka/mtl-da

    Evaluation Tools

    Direct AssessmentSentence Pairs Evaluation Tool
    https://github.com/bbc/gourmet-sentence-pairs-evaluation

    The goal of Direct Assessment is to evaluate a translation model by asking a human to compare the quality of a machine translated sentence to a human translated sentence where the human translation is assumed to be the gold standard

    Gap Fill Evaluation Tool
    https://github.com/bbc/gourmet-gap-fill-evaluation

    The goal of Gap Fill is to evaluate a translation model by asking a human to fill in the gaps in a sentence that has been translated by a human using the machine translation of the same sentence as a guide to what words should go in that sentence.

    Photo by Ankush Minda on Unsplash