ICLR 2021

In this talk, Yejin mainly talks about abduction and counterfactual reasoning and mentions that although they're different, both involve nonmonotonic reasoning with past context X and future constraint Z. Using language models such as GPT2 bc they are only good at conditioning on the past and we can incorporate the future by incorporating both past and future as the past with a special token in between. But this doesn't generalize well to out-of-domain distribution. So they propose to use back-prop as an inference time algorithm rather than training time only for abduction. For counterfactual reasoning, the same works just need to change the loss function to K-L divergence.