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A Game Theoretic Approach to Class-wise Selective Rationalization
[article]
2019
arXiv
pre-print
Selection of input features such as relevant pieces of text has become a common technique of highlighting how complex neural predictors operate. The selection can be optimized post-hoc for trained models or incorporated directly into the method itself (self-explaining). However, an overall selection does not properly capture the multi-faceted nature of useful rationales such as pros and cons for decisions. To this end, we propose a new game theoretic approach to class-dependent rationalization,
arXiv:1910.12853v1
fatcat:sj2krzjm5vgxnmvtcvgnacnskq