6 citations · 7 across the 5 of their papers we have counts for
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cs.AI2023★ 1 cited
D-Separation for Causal Self-Explanation
Wei Liu, Jun Wang, Haozhao Wang +4
Rationalization is a self-explaining framework for NLP models. Conventional work typically uses the maximum mutual information (MMI) criterion to find the rationale that is most in…
cs.LG2023★ 6 cited
Decoupled Rationalization with Asymmetric Learning Rates: A Flexible Lipschitz Restraint
Wei Liu, Jun Wang, Haozhao Wang +5
A self-explaining rationalization model is generally constructed by a cooperative game where a generator selects the most human-intelligible pieces from the input text as rationale…
cs.LG2023
MGR: Multi-generator Based Rationalization
Wei Liu, Haozhao Wang, Jun Wang +4
Rationalization is to employ a generator and a predictor to construct a self-explaining NLP model in which the generator selects a subset of human-intelligible pieces of the input…