  In natural language processing (NLP), text classification tasks are
increasingly fine-grained, as datasets are fragmented into a larger number of
classes that are more difficult to differentiate from one another. As a
consequence, the semantic structures of datasets have become more complex, and
model decisions more difficult to explain. Existing tools, suited for
coarse-grained classification, falter under these additional challenges. In
response to this gap, we worked closely with NLP domain experts in an iterative
design-and-evaluation process to characterize and tackle the growing
requirements in their workflow of developing fine-grained text classification
models. The result of this collaboration is the development of SemLa, a novel
visual analytics system tailored for 1) dissecting complex semantic structures
in a dataset when it is spatialized in model embedding space, and 2)
visualizing fine-grained nuances in the meaning of text samples to faithfully
explain model reasoning. This paper details the iterative design study and the
resulting innovations featured in SemLa. The final design allows contrastive
analysis at different levels by unearthing lexical and conceptual patterns
including biases and artifacts in data. Expert feedback on our final design and
case studies confirm that SemLa is a useful tool for supporting model
validation and debugging as well as data annotation.
