Before revision:  We translated two large English NLI datasets into Turkish and had a team of experts validate their quality. As examples of the new issues that these datasetshelp us address, we assess the value of Turkish-specific embeddings and the importance of morphological parsing for developing robust Turkish NLI models .
After revision:  We translated two large English NLI datasets into Turkish and had a team of experts validate their translation quality and fidelity to the original labels. Using these datasets, we address core issues of representation for Turkish NLI. We find that in-language embeddings are essential and that morphological parsing can be avoided where the training set is large. Finally, we show that models trained on our machine-translated datasets are successful on human-translated evaluation sets. We share all code, models, and data publicly .