Multilingual CheckList: Generation and Evaluation

Multilingual evaluation benchmarks usually contain limited high-resource languages and do not test models for specific linguistic capabilities.CheckList (Ribeiro et al., 2020) is a template-based evaluation approach that tests models for specific capabilities.The CheckList template creation process requires native speakers, posing a challenge in scaling to hundreds of languages.In this work, we explore multiple approaches to generate Multilingual Check-Lists.We device an algorithm -Template Extraction Algorithm (TEA) for automatically extracting target language CheckList templates from machine translated instances of a source language templates.We compare the TEA CheckLists with CheckLists created with different levels of human intervention.We further introduce metrics along the dimensions of cost, diversity, utility, and correctness to compare the CheckLists.We thoroughly analyze different approaches to creating Check-Lists in Hindi.Furthermore, we experiment with 9 more different languages.We find that TEA followed by human verification is ideal for scaling Checklist-based evaluation to multiple languages while TEA gives a good estimates of model performance.We release the code of TEA and the CheckLists created at aka.ms/multilingualchecklist