Request Correction

TranslateGemma for ES-EN Financial Reports: Exploring Adaptability to Variable-Sized Contexts

This paper explores bidirectional financial Machine Translation (MT) between Spanish and English, focusing on the specialized domain of annual reports from IBEX 35 companies. Fine-tuned models are compared against zero-shot scenarios through a series of experiments, testing factors such as prompting strategies and model size. On the one hand, this work studies a combination of existing fine-tuning strategies aimed at improving the adaptability of MT models to variable-sized contexts, and, on the other hand, it analyzes the limitations detected in current evaluation metrics. Results are mixed: fine-tuned models show an improvement in both short and long-context scenarios in traditional metrics, while zero-shot predictions are clearly favored by neural metrics. In fact, reference-free assessment of the source and the human reference received worse scores than the off-the-shelf prediction models. Consequently, fine-tuning on the human-made dataset hardly improves the neural metrics against zero-shot generations. This suggests that neural metrics tend to favor the fluency of MT generations and literalness over creativity, among other technical limitations regarding long-context adaptability. From a practical standpoint, the low Translation Edit Rate (TER) scores suggest that specialized fine-tuning remains the most viable path for companies to implement efficient Machine Translation Post-Editing (MTPE) workflows, given the stylistic alignment.