NMTVis - Neural Machine Translation Visualization System

DOI

NMTVis is a web-based visual analytics system to analyze, understand, and correct translations generated with neural machine translation. First, a document can be translated using a neural machine translation model (we support an LSTM-based and the Transformer architecture). Afterward, users can find mistranslated sentences, explore and correct these sentences and retrain the model to generate a better translation for the whole document. Our approach targets the correction of domain-specific documents.

You may find the most recent version of the source code on GitHub: https://github.com/MunzT/NMTVis

Trained models for translation from German to English and vice versa can be found here: https://doi.org/10.18419/darus-1850

Identifier
DOI https://doi.org/10.18419/darus-1849
Metadata Access https://darus.uni-stuttgart.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18419/darus-1849
Provenance
Creator Munz, Tanja ORCID logo; Väth, Dirk; Kuznecov, Paul; Vu, Ngoc Thang; Weiskopf, Daniel ORCID logo
Publisher DaRUS
Contributor Munz, Tanja
Publication Year 2021
Funding Reference Deutsche Forschungsgemeinschaft EXC 2075 - 390740016
Rights MIT License; info:eu-repo/semantics/openAccess; https://spdx.org/licenses/MIT.html
OpenAccess true
Contact Munz, Tanja (University of Stuttgart)
Representation
Resource Type Dataset
Format application/zip
Size 32868780
Version 1.0
Discipline Other