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dc.contributor.authorYousef, Tariqen_US
dc.contributor.authorJänicke, Stefanen_US
dc.contributor.editorAgus, Marcoen_US
dc.contributor.editorAigner, Wolfgangen_US
dc.contributor.editorHoellt, Thomasen_US
dc.description.abstractTranslation alignment plays a crucial role in various applications in natural language processing and digital humanities. With the recent advance in neural machine translation and contextualized language models, numerous studies have emerged on this topic, and several models and tools have been proposed. The performance of the proposed models has been always tested on standard benchmark data sets of different language pairs according to quantitative metrics such as Alignment Error Rate (AER) and F1. However, a detailed explanation on what alignment features contribute to these scores is missing. In order to allow analyzing the performance of alignment models, we present a visual analytics framework that aids researchers and developers in visualizing the output of their alignment models. We propose different visualization approaches that support assessing their own model's performance against alignment gold standards or in comparison to the performance of other models.en_US
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.subjectCCS Concepts: Human-centered computing --> Visual analytics; Visualization design and evaluation methods; Computing methodologies --> Machine translation
dc.subjectHuman centered computing
dc.subjectVisual analytics
dc.subjectVisualization design and evaluation methods
dc.subjectComputing methodologies
dc.subjectMachine translation
dc.titleVisual Evaluation of Translation Alignment Dataen_US
dc.description.seriesinformationEuroVis 2022 - Short Papers
dc.identifier.pages5 pages

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Attribution 4.0 International License
Except where otherwise noted, this item's license is described as Attribution 4.0 International License