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Flexible Generation of Natural Language Deductions
[article]
2021
arXiv
pre-print
An interpretable system for open-domain reasoning needs to express its reasoning process in a transparent form. Natural language is an attractive representation for this purpose -- it is both highly expressive and easy for humans to understand. However, manipulating natural language statements in logically consistent ways is hard: models must cope with variation in how meaning is expressed while remaining precise. In this paper, we describe ParaPattern, a method for building models to generate
arXiv:2104.08825v2
fatcat:b4dy552stja33pqqcpahxmkokq