  Humans have the capacity to draw common-sense inferences from natural
language: various things that are likely but not certain to hold based on
established discourse, and are rarely stated explicitly. We propose an
evaluation of automated common-sense inference based on an extension of
recognizing textual entailment: predicting ordinal human responses on the
subjective likelihood of an inference holding in a given context. We describe a
framework for extracting common-sense knowledge from corpora, which is then
used to construct a dataset for this ordinal entailment task. We train a neural
sequence-to-sequence model on this dataset, which we use to score and generate
possible inferences. Further, we annotate subsets of previously established
datasets via our ordinal annotation protocol in order to then analyze the
distinctions between these and what we have constructed.
