Year: 2021
Type: article
Abstract: 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.
Source: Information Visualization
Institutions: Swansea University, University of Nottingham
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,000
OpenAlex ID: W3194080709