# Data and Representation for Turkish Natural Language Inference

arXiv: https://arxiv.org/abs/2004.14963 · HTML: https://mlsanity.com/paper/2004.14963/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [JamPatoisNLI: A Jamaican Patois Natural Language Inference Dataset](https://mlsanity.com/paper/2212.03419/) (2022)
- [PrefDisco: Benchmarking Proactive Personalized Reasoning](https://mlsanity.com/paper/2510.00177/) (2025)

## Abstract (arXiv)

Large annotated datasets in NLP are overwhelmingly in English. This is an obstacle to progress in other languages. Unfortunately, obtaining new annotated resources for each task in each language would be prohibitively expensive. At the same time, commercial machine translation systems are now robust. Can we leverage these systems to translate English-language datasets automatically? In this paper, we offer a positive response for natural language inference (NLI) in Turkish. 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.