Ordinal Common-sense Inference
نویسندگان
چکیده
Humans have the capacity to draw commonsense 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 of subjective likelihood of an inference holding in a given context. We describe a framework for extracting common-sense knowledge for corpora, which is then used to construct a dataset for this ordinal entailment task, which we then use to train and evaluate a sequence to sequence neural network model. 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.
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عنوان ژورنال:
- TACL
دوره 5 شماره
صفحات -
تاریخ انتشار 2017