  Hand-curated natural language systems provide an inspectable, correctable
alternative to language systems based on machine learning, but maintaining them
requires considerable effort and expertise. Interactive Natural Language
Debugging (INLD) aims to lessen this burden by casting debugging as a reasoning
problem, asking the user a series of questions to diagnose and correct errors
in the system's knowledge. In this paper, we present work in progress on an
interactive error diagnosis system for the CNLU semantic parser. We show how
the first two stages of the INLD pipeline (symptom identification and error
localization) can be cast as a model-based diagnosis problem, demonstrate our
system's ability to diagnose semantic errors on synthetic examples, and discuss
design challenges and frontiers for future work.
