The proposed Incremental Decoding framework can be extended to other language pairs or domains by adapting its core principles to accommodate the unique characteristics of different languages and text types. Here are several strategies for such an extension:
Language-Specific Adaptation: Each language has its own syntactic and semantic structures. The framework can be modified to include language-specific pre-training datasets that reflect the linguistic nuances of the target language pair. For instance, incorporating monolingual corpora from diverse genres (technical, legal, conversational) can enhance the model's understanding of context and style.
Domain-Specific Training: Beyond literary translation, the framework can be tailored for specific domains such as legal, medical, or technical translation. This can be achieved by integrating domain-specific corpora during the Continual Pre-training and Supervised Fine-Tuning stages, allowing the model to learn the specialized vocabulary and context relevant to those fields.
Cross-Domain Transfer Learning: The framework can leverage transfer learning techniques to apply knowledge gained from one domain (e.g., literary translation) to another (e.g., technical translation). By fine-tuning the model on a smaller dataset from the new domain, it can retain the contextual awareness and stylistic consistency learned from literary texts.
Multilingual Contextualization: To handle multiple language pairs, the framework can incorporate a multilingual model that utilizes shared representations across languages. This would involve training on parallel corpora from various language pairs, allowing the model to generalize better across different linguistic contexts.
Enhanced Contextual Information: The framework can be improved by integrating additional contextual information, such as discourse markers or thematic elements, which are crucial in various domains. This would help maintain coherence and consistency in translations across different text types.
By implementing these strategies, the Incremental Decoding framework can effectively adapt to a wider range of languages and domains, enhancing its applicability and performance in diverse translation tasks.