6 citations · 13 across the 9 of their papers we have counts for
5 papers · 1 filter
Decoupling Representation and Knowledge for Few-Shot Intent Classification and Slot Filling
Jie Han, Yixiong Zou, Haozhao Wang +5
Few-shot intent classification and slot filling are important but challenging tasks due to the scarcity of finely labeled data. Therefore, current works first train a model on sour…
Enhancing the Rationale-Input Alignment for Self-explaining Rationalization
Wei Liu, Haozhao Wang, Jun Wang +4
Rationalization empowers deep learning models with self-explaining capabilities through a cooperative game, where a generator selects a semantically consistent subset of the input…
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…