Comprehensive Review of Turkish Paraphrase Datasets: A Linguistics View

Abstract

Datasets play a crucial role in advancing NLP tasks by providing resources for training and evaluating models. These datasets serve as the foundation for developing models that can understand and generate human language. However, little research has been performed on creating datasets in low-resource languages, such as Turkish, compared to English. This research focuses on paraphrase datasets, which is a limited area of study in NLP. To the best of our knowledge, nine Turkish paraphrase datasets are available. Accordingly, we provide a comprehensive review of Turkish paraphrase datasets by examining their scope, design, and linguistic properties. The pros and cons of the datasets are discussed, including their size, variety, and domain coverage limitations. Suggestions are presented for a more diverse, large-scale, and expandable Turkish paraphrase corpora that could better represent the language’s linguistic richness.

Keywords

• Turkish Paraphrase Datasets
• Turkish Corpus Design
• Turkish NLP

References

1. Alkurdi, B., Sarıoğlu, H. Y., & Amasyalı, M. F. (2022). Semantic similarity based filtering for Turkish paraphrase dataset creation. In M. Abbas & A. A. Freihat (Eds.), Proceedings of the 5th International Conference on Natural Language and Speech Processing (ICNLSP 2022) (pp. 119-127). Association for Computational Linguistics. google scholar
2. Alkurdi, B., Sarıoğlu, H. Y., & Amasyalı, M. F. (2024). Scaling Up Paraphrase Generation Datasets with Machine Translation and Semantic Similarity Filtering. In M. Abbas (Ed.), Practical Solutions for Diverse Real-World NLP Applications (pp. 21-36). Springer International Publishing. 10.1007/978-3-031-44260-5_2 google scholar
3. Bağcı, A., & Amasyalı, M. F. (2021). Comparison of Turkish Paraphrase Generation Models. In 2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) (pp. 1-6). IEEE. https://doi.org/10.1109/INISTA52262.2021.954833510.1109/INISTA52262.2021.9548335 google scholar
4. Bhagat, R., & Hovy, E. (2013). What is a paraphrase? Computational Linguistics, 39(3), 463-472. https://doi.org/10.1162/COLI_a_00166 google scholar
5. Biber, D. (1993). Representativeness in Corpus Design. Literary and Linguistic Computing, 8(4), 243-257. https://doi.org/10.1093/llc/8.4.243https://doi.org/10.1093/llc/8.4.243 google scholar
6. Biber, D., Conrad, S., & Reppen, R. (1998). Corpus Linguistics: Investigating Language Structure and Use. https://doi.org/10.1017/CBO9780511804489 google scholar
7. Binbir, M., & Aksoy, S. (2021). Paraphrase generation for Turkish language. Retrieved Aralık 22, 2024, from https://github.com/sercaksoy/MT5-Turkish-Paraphrase-Generation/blob/main/paper.pdf google scholar
8. Cambridge University Press. (n.d.). Corpus. Retrieved Aralık 5, 2024, from https://dictionary.cambridge.org/dictionary/english/corpus google scholar

9. Can, F., Kocberber, S., Bağlıoğlu, Ö., Kardaş, S., Öcalan, H. Ç., & Uyar, E. (2010). New event detection and topic tracking in Turkish. Journal of the American Society for Information Science and Technology, 61(4), 802-819. https://doi.org/10.1002/asi.21264 google scholar
10. Clark, E. V. (1992). Conventionality and contrast: Pragmatic principles with lexical consequences. In A. Lehrer & E. F. Kittay (Eds.), Frames, Fields, and Contrasts: New Essays in Semantic Lexical Organization (pp. 171-188). New York: Lawrence Erlbaum Associates. google scholar
11. De Beaugrande, R. A., & Dressler, W. U. (1981). Introduction to Text Linguistics (Vol. 1). Longman. google scholar
12. Demir, S., El-Kahlout, İ. D., Ünal, E., & Kaya, H. (2012). Turkish Paraphrase Corpus. In N. Calzolari, K. Choukri, T. Declerck, M. U. Doğan, B. Maegaard, J. Mariani, A. Moreno, J. Odijk, & S. Piperidis (Eds.), Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12) (pp. 4087-4091). European Language Resources Association (ELRA). google scholar
13. Dolan, W. B., & Brockett, C. (2005). Automatically constructing a corpus of semantic paraphrases. In Proceedings of the Third International Workshop on Paraphrasing (IWP2005). google scholar
14. Eyecioğlu, A., & Keller, B. (2018). Constructing a Turkish Corpus for Paraphrase Identification and Semantic Similarity. In A. Gelbukh (Ed.), Computational Linguistics and Intelligent Text Processing (pp. 588-599). Springer International Publishing. https://doi.org/10.1007/978-3-319-75477-2_42 google scholar
15. Hirst, G. (2003). Paraphrasing Paraphrased. Keynote Address for the 2nd International Workshop on Paraphrasing: Paraphrase Acquisition and Applications. google scholar
16. İmer, K., Kocaman, A., & Özsoy, A. S. (2019). Dilbilim Sözlüğü, 3rd ed. Boğaziçi Üniversitesi Yayınevi. google scholar
17. Karaoğlan, B., Kışla, T., & Kumova Metin, S. (2018). Description of Turkish Paraphrase Corpus Structure and Generation Method. In A. Gelbukh (Ed.), Computational Linguistics and Intelligent Text Processing (pp. 208-217). Springer International Publishing. google scholar
18. Karaoğlan, B., Kışla, T., Kumova Metin, S., Hürriyetoğlu, U., & Soleymanzadeh, K. (2016). Using multiple metrics in automatically building Turkish paraphrase corpus. In A. Gelbukh (Ed.), Research in Computing Science, 117, pp. 75-83). google scholar
19. Lison, P., & Tiedemann, J. (2016). OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16). European Language Resources Association (ELRA). pp. 923-929. google scholar
20. McEnery, T., & Wilson, A. (2001). Corpus Linguistics: An Introduction. Edinburgh University Press. google scholar
21. Scherrer, Y. (2020). TaPaCo: A Corpus of Sentential Paraphrases for 73 Languages. In: Calzolari, N., Béchet, F., Blache, P., Choukri, K., Cieri, C., Declerck, T., Goggi, S., Isahara, H., Mariani, J., Mazo, H., Moreno, A., Odijk, J., & Piperidis, S. (Eds.), Proceedings of the Twelfth Language Resources and Evaluation Conference (pp. 6868-6873). European Language Resources Association. google scholar
22. Sinclair, J. (2005). Meaning in the framework of corpus linguistics. Lexicographica, 20(2004), 20-32. https://doi.org/10.1515/9783484604674.20 google scholar
23. Tekgöz, H., & İlhan Omurca, S. (2023). Turkish and English datasets in paraphrase generation task with BART model. In Ş. Taşdemir (Ed.), International Conference on Engineering Technologies (ICENTE'23) (pp. 45-48). Selçuk Üniversitesi. google scholar
24. Tiedemann, J. (2012). Parallel data, tools and interfaces in OPUS. In N. Calzolari, K. Choukri, T. Declerck, M. U. Doğan, B. Maegaard, J. Mariani, A. Moreno, J. Odijk, & S. Piperidis (Eds.), Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12) (pp. 2214-2218). European Language Resources Association (ELRA). google scholar
25. Yazıcı, E., & Amasyalı, M. F. (2011). Kavramlar Arası Anlamsal İlişkilerin Türkçe Sözlük Tanımları Kullanılarak Otomatik Olarak Çıkartılması. EMO Bilimsel Dergi, 1(1), 1-13. google scholar