Context-aware and Style-related Incremental Decoding framework for Discourse-Level Literary Translation
This addresses the problem of maintaining coherence and style in document-level literary translation for machine translation systems, though it is incremental as it builds on existing models like Chinese-Llama2.
The paper tackled the challenge of translating literary texts by proposing a Context-aware and Style-related Incremental Decoding framework, which improved sentence-level and document-level BLEU scores for Chinese-English translation.
This report outlines our approach for the WMT24 Discourse-Level Literary Translation Task, focusing on the Chinese-English language pair in the Constrained Track. Translating literary texts poses significant challenges due to the nuanced meanings, idiomatic expressions, and intricate narrative structures inherent in such works. To address these challenges, we leveraged the Chinese-Llama2 model, specifically enhanced for this task through a combination of Continual Pre-training (CPT) and Supervised Fine-Tuning (SFT). Our methodology includes a novel Incremental Decoding framework, which ensures that each sentence is translated with consideration of its broader context, maintaining coherence and consistency throughout the text. This approach allows the model to capture long-range dependencies and stylistic elements, producing translations that faithfully preserve the original literary quality. Our experiments demonstrate significant improvements in both sentence-level and document-level BLEU scores, underscoring the effectiveness of our proposed framework in addressing the complexities of document-level literary translation.