  Machine Learning (ML) has been widely used in Natural Language Processing
(NLP) applications. A fundamental assumption in ML is that training data and
real-world data should follow a similar distribution. However, a deployed ML
model may suffer from out-of-distribution (OOD) issues due to distribution
shifts in the real-world data. Though many algorithms have been proposed to
detect OOD data from text corpora, there is still a lack of interactive tool
support for ML developers. In this work, we propose DeepLens, an interactive
system that helps users detect and explore OOD issues in massive text corpora.
Users can efficiently explore different OOD types in DeepLens with the help of
a text clustering method. Users can also dig into a specific text by inspecting
salient words highlighted through neuron activation analysis. In a
within-subjects user study with 24 participants, participants using DeepLens
were able to find nearly twice more types of OOD issues accurately with 22%
more confidence compared with a variant of DeepLens that has no interaction or
visualization support.
