In this thesis, I explore the problem of low-resource domain adaptation for jihadi discourse. Due to the limited availability of annotated parallel data, developing accurate and effective models in this domain poses a challenging task. To address this issue, I propose a method that leverages a small in-domain manually created corpus and a synthetic corpus created from monolingual data using back-translation. I evaluate the approach by fine-tuning a pre-trained language model on different proportions of real and synthetic data and measuring its performance on a held-out test set. My experiments show that fine-tuning a model on one-fifth real parallel data and synthetic parallel data effectively reduces occurrences of over-translation and bolsters the model's ability to translate in-domain terminology. My findings suggest that synthetic data can be a valuable resource for low-resource domain adaptation, especially when real parallel data is difficult to obtain. The proposed method can be extended to other low-resource domains where annotated data is scarce, potentially leading to more accurate models and better translation of these domains.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-503371 |
| Date | January 2023 |
| Creators | Tollersrud, Thea |
| Publisher | Uppsala universitet, Institutionen för lingvistik och filologi |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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