Code for Handling Divergent Reference Texts when Evaluating Table-to-Text Generation (Dhingra et al. 2019)

Code for Controlling Hallucinations at Word Level in Data-to-Text Generation (C. Rebuffel, M. Roberti, L. Soulier, G. Scoutheeten, R. Cancelliere, P. Gallinari)

Code for PARENTing via Model-Agnostic Reinforcement Learning to Correct Pathological Behaviors in Data-to-Text Generation (Rebuffel, Soulier, Scoutheeten, Gallinari; INLG 2020)

Research project: Evaluate unsupervised text deidentification methods from "Unsupervised Text Deidentification" by Morris et al., using the WikiBio dataset and fine-tuned RoBERTa models. The goal is to compare our results with the paper’s findings.