| Autor: |
David De-Fitero-Dominguez, Eva Garcia-Lopez, Antonio Garcia-Cabot, Jesus-Angel Del-Hoyo-Gabaldon, Antonio Moreno-Cediel |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 12, Pp 25580-25589 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3361673 |
| Popis: |
In recent years, transformer language models have made a significant impact on automatic text generation. This study focuses on the task of distractor generation in Spanish using a fine-tuned multilingual text-to-text model, namely mT5. Our method outperformed established baselines based on LSTM networks, confirming the effectiveness of Transformer architectures in such NLP tasks. While comparisons with other Transformer-based solutions yielded diverse outcomes based on the metric of choice, our method notably achieved superior results on the ROUGE metric compared to the GPT-2 approach. Although traditional evaluation metrics such as BLEU and ROUGE are commonly used, this paper argues for more context-sensitive metrics given the inherent variability in acceptable distractor generation results. Among the contributions of this research is a comprehensive comparison with other methods, an examination of the potential drawbacks of multilingual models, and the introduction of alternative evaluation metrics. Future research directions, derived from our findings and a review of related works are also suggested, with a particular emphasis on leveraging other language models and Transformer architectures. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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