Data and Representation for Turkish Natural Language Inference
This addresses the problem of limited NLP resources for non-English languages like Turkish, though it is incremental as it applies existing translation methods to a new language.
The authors tackled the lack of annotated datasets for Turkish natural language inference by automatically translating English NLI datasets and validating them, showing that models trained on these datasets perform well on human-translated evaluation sets.
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.