Abstract
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.- Anthology ID:
- 2020.emnlp-main.662
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8253–8267
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.662
- DOI:
- 10.18653/v1/2020.emnlp-main.662
- Cite (ACL):
- Emrah Budur, Rıza Özçelik, Tunga Gungor, and Christopher Potts. 2020. Data and Representation for Turkish Natural Language Inference. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8253–8267, Online. Association for Computational Linguistics.
- Cite (Informal):
- Data and Representation for Turkish Natural Language Inference (Budur et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2020.emnlp-main.662.pdf
- Code
- boun-tabi/NLI-TR
- Data
- NLI-TR, MultiNLI, SNLI, XNLI