  Long-form answers, consisting of multiple sentences, can provide nuanced and
comprehensive answers to a broader set of questions. To better understand this
complex and understudied task, we study the functional structure of long-form
answers collected from three datasets, ELI5, WebGPT and Natural Questions. Our
main goal is to understand how humans organize information to craft complex
answers. We develop an ontology of six sentence-level functional roles for
long-form answers, and annotate 3.9k sentences in 640 answer paragraphs.
Different answer collection methods manifest in different discourse structures.
We further analyze model-generated answers -- finding that annotators agree
less with each other when annotating model-generated answers compared to
annotating human-written answers. Our annotated data enables training a strong
classifier that can be used for automatic analysis. We hope our work can
inspire future research on discourse-level modeling and evaluation of long-form
QA systems.
