Abstract: A method trains an inference model on two-hop NLI problems that include a first and second premise and a hypothesis, and further includes generating, by the model using hypothesis reduction, an explanation from an input premise and an input hypothesis, for an input single hop NLI problem. The learning step determines a distribution over extraction starting positions and lengths from within the first premise and hypothesis of a two-hop NLI problem. The learning step k extraction output slots with combinations of words from the first premise of the two-hop NLI problem and fills another extraction output slots with combinations of words from the hypothesis of the two-hop NLI problem. The learning step trains a sequence model by using the extraction output slots and the other extraction output slots together with the second premise as an input to a single-hop NLI classifier to output a label of the two-hop NLI problem.

Abstract: A method trains an inference model on two-hop NLI problems that include a first and second premise and a hypothesis, and further includes generating, by the model using hypothesis reduction, an explanation from an input premise and an input hypothesis, for an input single hop NLI problem. The learning step determines a distribution over extraction starting positions and lengths from within the first premise and hypothesis of a two-hop NLI problem. The learning step k extraction output slots with combinations of words from the first premise of the two-hop NLI problem and fills another extraction output slots with combinations of words from the hypothesis of the two-hop NLI problem. The learning step trains a sequence model by using the extraction output slots and the other extraction output slots together with the second premise as an input to a single-hop NLI classifier to output a label of the two-hop NLI problem.