  We introduce the new Birds-to-Words dataset of 41k sentences describing
fine-grained differences between photographs of birds. The language collected
is highly detailed, while remaining understandable to the everyday observer
(e.g., "heart-shaped face," "squat body"). Paragraph-length descriptions
naturally adapt to varying levels of taxonomic and visual distance---drawn from
a novel stratified sampling approach---with the appropriate level of detail. We
propose a new model called Neural Naturalist that uses a joint image encoding
and comparative module to generate comparative language, and evaluate the
results with humans who must use the descriptions to distinguish real images.
  Our results indicate promising potential for neural models to explain
differences in visual embedding space using natural language, as well as a
concrete path for machine learning to aid citizen scientists in their effort to
preserve biodiversity.
