2 citations · 3 across the 4 of their papers we have counts for
4 papers
Measuring algorithmic interpretability: A human-learning-based framework and the corresponding cognitive complexity score
John P. Lalor, Hong Guo
Algorithmic interpretability is necessary to build trust, ensure fairness, and track accountability. However, there is no existing formal measurement method for algorithmic interpr…
Dynamic Data Selection for Curriculum Learning via Ability Estimation
John P. Lalor, Hong Yu
Curriculum learning methods typically rely on heuristics to estimate the difficulty of training examples or the ability of the model. In this work, we propose replacing difficulty…
Efficient Semi-Supervised Learning for Natural Language Understanding by Optimizing Diversity
Eunah Cho, He Xie, John P. Lalor +2
Expanding new functionalities efficiently is an ongoing challenge for single-turn task-oriented dialogue systems. In this work, we explore functionality-specific semi-supervised le…
Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds
John P. Lalor, Hao Wu, Hong Yu
Incorporating Item Response Theory (IRT) into NLP tasks can provide valuable information about model performance and behavior. Traditionally, IRT models are learned using human res…