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ZENODO
Dataset . 2019
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2019
License: CC BY
Data sources: Datacite
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doclevel-MT-benchmark-discoMT2019

Authors: Tiedemann, Jörg; Scherrer, Yves;

doclevel-MT-benchmark-discoMT2019

Abstract

This release contains data sets for experiments with document-level machine translation. The data sets have been used in previous studies and provided here for replicability and comparison with other systems. The data sets are taken from the English-German news translation task at WMT 2019 and the English-German bitext in the OpenSubtitles collection v2016 from OPUS. All data sets are sentence aligned with corresponding lines being aligned to each other. Document boundaries are marked with empty lines (on both sides of the parallel corpus). The data set has been used in the following publication: @inproceedings{scherrer-tiedemann-loaiciga-2019, title = "Analysing concatenation approaches to document-level NMT in two different domains", author = {Scherrer, Yves and Tiedemann, J{\"o}rg and Lo{\'a}iciga, Sharid}, booktitle = "Proceedings of the Third Workshop on Discourse in Machine Translation", month = nov, year = "2019", address = "Hong-Kong", publisher = "Association for Computational Linguistics", } Please, cite that paper if you use the data set in your own work.

{"references": ["Scherrer, Tiedemann and Lo\u00e1iciga: \"Analysing concatenation approaches to document-level NMT in two different domains\", in Proceedings of DiscoMT2019 at EMNLP 2019, Hong-Kong"]}

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Keywords

language technology, natural language processing, NLP, machine translation

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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