Cited by Lee Sonogan
Abstract by Mohammad Alharbi, Matthew Roach, Tom Cheesman, …
In general, Natural Language Processing (NLP) algorithms exhibit black-box behavior. Users input text and output are provided with no explanation of how the results are obtained. In order to increase understanding and trust, users value transparent processing which may explain derived results and enable understanding of the underlying routines. Many approaches take an opaque approach by default when designing NLP tools and do not incorporate a means to steer and manipulate the intermediate NLP steps. We present an interactive, customizable, visual framework that enables users to observe and participate in the NLP pipeline processes, explicitly manipulate the parameters of each step, and explore the result visually based on user preferences. The visible NLP (VNLP) pipeline design is then applied to a text similarity application to demonstrate the utility and advantages of a visible and transparent NLP pipeline in supporting users to understand and justify both the process and results. We also report feedback on our framework from a modern languages expert.
Publication: Information Visualization (Peer-Review Journal)
Pub Date: Aug 13, 2021 Doi: https://doi.org/10.1177/14738716211038898
Keywords: Text alignment, parallel translations, text visualization
https://journals.sagepub.com/doi/full/10.1177/14738716211038898 (Plenty more sections and references in this research article)
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