  We train a language model (LM) to robustly answer multistep questions by
generating and answering sub-questions. We propose Chain-of-Questions, a
framework that trains a model to generate sub-questions and sub-answers one at
a time by leveraging human annotated question decomposition meaning
representation (QDMR). The key technical challenge is that QDMR only contains
sub-questions but not answers to those sub-questions, so we treat sub-answers
as latent variables and optimize them using a novel dynamic mixture of Hard-EM
and MAPO. Chain-of-Questions greatly outperforms strong neuro-symbolic methods
by 9.0 F1 on DROP contrast set, and outperforms GPT-3.5 by 24.3 F1 on HOTPOTQA
adversarial set, thus demonstrating the effectiveness and robustness of our
framework.
