Discriminative  question  answering  models  can  overfit  to  superficial  biases  in datasets,  because their loss function saturates when any clue makes the answer likely.   We introduce generative models of the joint distribution of questions and answers, which are trained to explain the whole question, not just to answer it. Our  question  answering  (QA)  model  is  implemented  by  learning  a  prior  over answers,  and  a  conditional  language  model  to  generate  the  question  given  the answer—allowing scalable and interpretable many-hop reasoning as the question is generated word-by-word.   Our model achieves competitive performance with specialised discriminative models on the SQUAD and CLEVR benchmarks, indicating that it is a more general architecture for language understanding and reasoning than previous work. The model greatly improves generalisation both from biased training data and to adversarial testing data, achieving a new state-of-the-art on ADVERSARIAL SQUAD. We will release our code.
Question answering models that model the joint distribution of questions and answers can learn more than discriminative models This paper proposes a generative approach to textual and visual QA, where a joint distribution over the question and answer space given the context is learned, which captures more complex relationships. This paper introduces a generative model for question answering and proposes to model p(q,a|c), factorized as p(a|c) * p(q|a,c).  The authors proposes a generative QA model, which optimizes jointly the distribution of questions and answering given a document/context. 