6 citations · 15 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Structure Diagram Recognition in Financial Announcements
Meixuan Qiao, Jun Wang, Junfu Xiang +2
Accurately extracting structured data from structure diagrams in financial announcements is of great practical importance for building financial knowledge graphs and further improv…
FR: Folded Rationalization with a Unified Encoder
Wei Liu, Haozhao Wang, Jun Wang +3
Conventional works generally employ a two-phase model in which a generator selects the most important pieces, followed by a predictor that makes predictions based on the selected p…