Generating Textual Explanations for Machine Learning Models Performance: A Table-to-Text Task

no code implementations • LREC 2022 • Isaac Ampomah, James Burton, Amir Enshaei, Noura Al Moubayed

This paper proposes a new natural language generation (NLG) task where neural models are trained to generate textual explanations, analytically describing the classification performance of ML models based on the metrics’ scores reported in the tables.

Classification Data-to-Text Generation