Ordinal Common-sense Inference

Understand

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.