Language models in automated essay scoring: Insights for the Turkish language

Yıl 2023, Cilt: 10 Sayı: Special Issue, 149 - 163, 27.12.2023

Tahereh Firoozi Okan Bulut Mark Gierl

https://doi.org/10.21449/ijate.1394194

Öz

The proliferation of large language models represents a paradigm shift in the landscape of automated essay scoring (AES) systems, fundamentally elevating their accuracy and efficacy. This study presents an extensive examination of large language models, with a particular emphasis on the transformative influence of transformer-based models, such as BERT, mBERT, LaBSE, and GPT, in augmenting the accuracy of multilingual AES systems. The exploration of these advancements within the context of the Turkish language serves as a compelling illustration of the potential for harnessing large language models to elevate AES performance in in low-resource linguistic environments. Our study provides valuable insights for the ongoing discourse on the intersection of artificial intelligence and educational assessment.

Anahtar Kelimeler

Automated essay scoring, Word embedding, Transformers, BERT, Turkish AES

Kaynakça

• Akın, A.A., & Akın, M.D. (2007). Zemberek, an open source NLP framework for Turkic languages. Structure, 10(2007), 1-5.
• Arslan, R.S., & Barişçi, N. (2020). A detailed survey of Turkish automatic speech recognition. Turkish Journal of Electrical Engineering and Computer Sciences, 28(6), 3253-3269.
• Bird, S. (2006, July). NLTK: the natural language toolkit. In Proceedings of the COLING/ACL 2006 Interactive Presentation Sessions (pp. 69-72).
• Black, S., Biderman, S., Hallahan, E., Anthony, Q., Gao, L., Golding, L., ... & Weinbach, S. (2022). Gpt-neox-20b: An open-source autoregressive language model. arXiv preprint arXiv:2204.06745.
• Bojanowski, P., Grave, E., Joulin, A., and Mikolov, T. (2017). Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 5, 135–146.
• Bouschery, S.G., Blazevic, V., & Piller, F.T. (2023). Augmenting human innovation teams with artificial intelligence: Exploring transformer‐based language models. Journal of Product Innovation Management, 40(2), 139-153.
• Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901.
• Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., ... & Zhang, Y. (2023). Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712.
• Cai, D., He, X., Wang, X., Bao, H., & Han, J. (2009, June). Locality preserving nonnegative matrix factorization. In Twenty-first International Joint Conference on Artificial Intelligence.
• Cetin, M.A., & Ismailova, R. (2019). Assisting tool for essay grading for Turkish language instructors. MANAS Journal of Engineering, 7(2), 141-146.
• Chi, Z., Dong, L., Wei, F., Yang, N., Singhal, S., Wang, W., ... & Zhou, M. (2020). InfoXLM: An information-theoretic framework for cross-lingual language model pre-training. arXiv preprint arXiv:2007.07834.
• Conneau, A., & Lample, G. (2019). Cross-lingual language model pretraining. Advances in Neural Information Processing Systems, 32.
• Devlin, J., Chang, M.W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
• Dikli, S. (2006). Automated essay scoring. Turkish Online Journal of Distance Education, 7(1), 49-62.
• Firoozi, T., Bulut, O., Epp, C.D., Naeimabadi, A., & Barbosa, D. (2022). The effect of fine-tuned word embedding techniques on the accuracy of automated essay scoring systems using Neural networks. Journal of Applied Testing Technology, 23, 21-29.
• Firoozi, T., & Gierl, M.J. (in press). Scoring multilingual essays using transformer-based models. Invited chapter to appear in M. Shermis & J. Wilson (Eds.), The Routledge International Handbook of Automated Essay Evaluation. New York: Routledge.
• Firoozi, T., Mohammadi, H., & Gierl, M.J. (2023). Using Active Learning Methods to Strategically Select Essays for Automated Scoring. Educational Measurement: Issues and Practice, 42(1), 34-43.
• Feng, F., Yang, Y., Cer, D., Arivazhagan, N., & Wang, W. (2020). Language-agnostic BERT sentence embedding. arXiv preprint arXiv:2007.01852.
• Fleckenstein, J., Meyer, J., Jansen, T., Keller, S., & Köller, O. (2020). Is a long essay always a good essay? The effect of text length on writing assessment. Frontiers in Psychology, 11, 562462.
• Gezici, G., & Yanıkoğlu, B. (2018). Sentiment analysis in Turkish. In K. Oflazer & M. Saraçlar (Eds.) Turkish Natural Language Processing. Theory and Applications of Natural Language Processing (pp. 255-271). Springer, Cham.
• Graesser, A.C., McNamara, D.S., Louwerse, M.M., & Cai, Z. (2004). Coh-Metrix: Analysis of text on cohesion and language. Behavior Research Methods, Instruments, & Computers, 36(2), 193-202.
• Han, T., & Sari, E. (2022). An investigation on the use of automated feedback in Turkish EFL students’ writing classes. Computer Assisted Language Learning, 1-24.
• Hochreiter, S., Bengio, Y., Frasconi, P., & Schmidhuber, J. (2001). Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. Neural Computation, 9(8):1735–1780.
• Hussein, M.A., Hassan, H., & Nassef, M. (2019). Automated language essay scoring systems: A literature review. PeerJ Computer Science, 5, e208.
• Kavi, D. (2020). Turkish Text Classification: From Lexicon Analysis to Bidirectional Transformer. arXiv preprint arXiv:2104.11642.
• Kenton, J.D.M.W.C., & Toutanova, L.K. (2019, June). Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of naacL-HLT, 1(2).
• Koskenniemi K (1983) Two-level morphology: A general computational model for word-form recognition and production. PhD dissertation, University of Helsinki, Helsinki.
• Kuyumcu, B., Aksakalli, C., & Delil, S. (2019, June). An automated new approach in fast text classification (fastText) A case study for Turkish text classification without pre-processing. In Proceedings of the 2019 3rd International Conference on Natural Language Processing and Information Retrieval (pp. 1-4).
• Liu, P., Joty, S., & Meng, H. (2015, September). Fine-grained opinion mining with recurrent neural networks and word embeddings. In Proceedings of the 2015 Conference on Empirical Methods İn Natural Language Processing (pp. 1433-1443).
• MacNeil, S., Tran, A., Mogil, D., Bernstein, S., Ross, E., & Huang, Z. (2022, August). Generating diverse code explanations using the gpt-3 large language model. In Proceedings of the 2022 ACM Conference on International Computing Education Research-Volume 2 (pp. 37-39).
• Mayer, C.W., Ludwig, S., & Brandt, S. (2023). Prompt text classifications with transformer models: An exemplary introduction to prompt-based learning with large language models. Journal of Research on Technology in Education, 55(1), 125-141.
• Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems, 26.
• Mizumoto, A., & Eguchi, M. (2023). Exploring the potential of using an AI language model for automated essay scoring. Research Methods in Applied Linguistics, 2(2), 100050.
• Oflazer, K., & Saraçlar, M. (Eds.). (2018). Turkish natural language processing. Springer International Publishing.
• Pennington, J., Socher, R., & Manning, C. D. (2014, October). Glove: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 1532-1543).
• Ramesh, D., & Sanampudi, S.K. (2022). An automated essay scoring systems: a systematic literature review. Artificial Intelligence Review, 55(3), 2495-2527.
• Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI Blog, 1(8), 9.
• Ranathunga, S., Lee, E.S.A., Prifti Skenduli, M., Shekhar, R., Alam, M., & Kaur, R. (2023). Neural machine translation for low-resource languages: A survey. ACM Computing Surveys, 55(11), 1-37.
• Rodriguez, P.U., Jafari, A., & Ormerod, C.M. (2019). Language models and automated essay scoring. arXiv preprint arXiv:1909.09482.
• Roshanfekr, B., Khadivi, S., & Rahmati, M. (2017). Sentiment analysis using deep learning on Persian texts. 2017 Iranian Conference on Electrical Engineering (ICEE).
• Singh, S., & Mahmood, A. (2021). The NLP cookbook: modern recipes for transformer based deep learning architectures. IEEE Access, 9, 68675-68702.
• Uysal, I., & Doğan, N. (2021). How Reliable Is It to Automatically Score Open-Ended Items? An Application in the Turkish Language. Journal of Measurement and Evaluation in Education and Psychology, 12(1), 28-53.
• Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
• Williamson, D.M., Xi, X., & Breyer, F.J. (2012). A framework for evaluation and use of automated scoring. Educational Measurement: Issues and Practice, 31(1), 2-13.
• Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., ... & Rush, A.M. (2020, October). Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (pp. 38-45).
• Yang, R., Cao, J., Wen, Z., Wu, Y., & He, X. (2020, November). Enhancing automated essay scoring performance via fine-tuning pre-trained language models with combination of regression and ranking. In Findings of the Association for Computational Linguistics: EMNLP 2020 (pp. 1560-1569).