  Question answering (QA) is an important use case on voice assistants. A
popular approach to QA is extractive reading comprehension (RC) which finds an
answer span in a text passage. However, extractive answers are often unnatural
in a conversational context which results in suboptimal user experience. In
this work, we investigate conversational answer generation for QA. We propose
AnswerBART, an end-to-end generative RC model which combines answer generation
from multiple passages with passage ranking and answerability. Moreover, a
hurdle in applying generative RC are hallucinations where the answer is
factually inconsistent with the passage text. We leverage recent work from
summarization to evaluate factuality. Experiments show that AnswerBART
significantly improves over previous best published results on MS MARCO 2.1
NLGEN by 2.5 ROUGE-L and NarrativeQA by 9.4 ROUGE-L.
