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Part 1: Document Description
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Citation |
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Title: |
NMTVis - Extended Neural Machine Translation Visualization System |
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Identification Number: |
doi:10.18419/darus-2124 |
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Distributor: |
DaRUS |
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Date of Distribution: |
2022-01-26 |
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Version: |
1 |
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Bibliographic Citation: |
Munz, Tanja; Väth, Dirk; Kuznecov, Paul; Vu, Ngoc Thang; Weiskopf, Daniel, 2022, "NMTVis - Extended Neural Machine Translation Visualization System", https://doi.org/10.18419/darus-2124, DaRUS, V1 |
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Citation |
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Title: |
NMTVis - Extended Neural Machine Translation Visualization System |
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Identification Number: |
doi:10.18419/darus-2124 |
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Authoring Entity: |
Munz, Tanja (University of Stuttgart) |
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Väth, Dirk (University of Stuttgart) |
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Kuznecov, Paul (University of Stuttgart) |
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Vu, Ngoc Thang (University of Stuttgart) |
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Weiskopf, Daniel (University of Stuttgart) |
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Grant Number: |
EXC 2075 - 390740016 |
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Distributor: |
DaRUS |
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Access Authority: |
Munz, Tanja |
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Depositor: |
Munz, Tanja |
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Date of Deposit: |
2021-12-20 |
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Holdings Information: |
https://doi.org/10.18419/darus-2124 |
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Study Scope |
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Keywords: |
Computer and Information Science, Visual Analytics, Neural Machine Translation, Machine Learning, Transformer, Long Short-Term Memory, Beam Search, Attention |
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Abstract: |
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. <i>This extended version of our visual analytics system provides additional visualization and interaction techniques as well as scripts for computer-based evaluation of our approach.</i> You can find important information about our system <a href="https://github.com/MunzT/NMTVis/blob/nmtvis_v2.0/README.md">here</a> and an introduction to our system <a href="https://github.com/MunzT/NMTVis/blob/nmtvis_v2.0/INTRO.md">here</a>. |
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Notes: |
You may find the most recent version of the source code on GitHub: <a href="https://github.com/MunzT/NMTVis">https://github.com/MunzT/NMTVis</a> <br> Trained models for translation from German to English and vice versa can be found here: <a href="https://doi.org/10.18419/darus-1850">https://doi.org/10.18419/darus-1850</a> |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
T. Munz, D. Väth, P. Kuznecov, N. T. Vu, and D. Weiskopf. "Visualization-based improvement of neural machine translation", Computers & Graphics, 2021. |
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Identification Number: |
10.1016/j.cag.2021.12.003 |
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Bibliographic Citation: |
T. Munz, D. Väth, P. Kuznecov, N. T. Vu, and D. Weiskopf. "Visualization-based improvement of neural machine translation", Computers & Graphics, 2021. |
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Label: |
nmtvis_v2.0.zip |
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Text: |
Source code of our visual analytics system |
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Notes: |
application/zip |