Studying variation among time-evolved translations is a valuable research area for cultural heritage. Understanding how and why translations vary reveals cultural, ideological, and even political influences on literature as well as author relations. In this article, we introduce a novel integrated visual application to support distant and close reading of a collection of Othello translations. We present a new interactive application that provides an alignment overview of all the translations and their correspondences in parallel with smooth zooming and panning capability to integrate distant and close reading within the same view. We provide a range of filtering and selection options to customize the alignment overview as well as focus on specific subsets. Selection and filtering are responsive to expert user preferences and update the analytical text metrics interactively. Also, we introduce a customized view for close reading which preserves the history of selections and the alignment overview state and enables backtracing and re-examining them. Finally, we present a new Term-Level Comparisons view (TLC) to compare and convey relative term weighting in the context of an alignment. Our visual design is guided by, used and evaluated by a domain expert specialist in German translations of Shakespeare.

Digital humanities and translation scholars utilize off-the-shelf tools to align multiple related translations. These tools generally rely solely on domain expert knowledge and do not exploit the recent advancements in computational linguistics and text mining. This paper presents AlignVis, a visual tool that provides a semi-automatic alignment framework to align multiple translations. It presents the results of using text similarity measurements and enables the user to create, verify, and edit alignments using a novel visual interface. The design consists of three main components: the alignment editor canvas, the post-edit area, and the user options panel. AlignVis exploits both close and distant reading and is designed to help digital humanities and translation scholars enhance the process of text alignment for multiple translations. The design of AlignVis is driven by iterative discussions with the domain expert which resulted in five benefits: presenting an overview of the aligned translations, support for multiple alignments, enhancement and acceleration of the alignment process, alignment refinement, and testing different similarity measurements. We evaluate AlignVis with domain expert feedback and a comparison with a standard alignment tool and computational and visual alignment tools.