  Long-form question answering (LFQA) tasks require retrieving the documents
pertinent to a query, using them to form a paragraph-length answer. Despite
considerable progress in LFQA modeling, fundamental issues impede its progress:
i) train/validation/test dataset overlap, ii) absence of automatic metrics and
iii) generated answers not being "grounded" in retrieved documents. This work
addresses every one these critical bottlenecks, contributing natural language
inference/generation (NLI/NLG) methods and metrics that make significant
strides to their alleviation.
