We explore the impact of multi-source input strategies on machine translation (MT) quality, comparing GPT-4o, a large language model (LLM), with a traditional multilingual neural machine translation (NMT) system. Using intermediate language translations as contextual cues, we evaluate their effectiveness in enhancing English and Chinese translations into Portuguese. Results suggest that contextual information significantly improves translation quality for domain-specific datasets and potentially for linguistically distant language pairs, with diminishing returns observed in benchmarks with high linguistic variability. Additionally, we demonstrate that shallow fusion, a multi-source approach we apply within the NMT system, shows improved results when using high-resource languages as context for other translation pairs, highlighting the importance of strategic context language selection.

本研究解决了机器翻译质量提升的关键问题，探讨了多源输入策略的影响。我们提出通过中介语言翻译作为情境线索，显著提升英中翻译到葡萄牙语的质量，尤其在特定领域数据集和语言距离较大的情况下。最重要的发现是，战略性选择情境语言能够显著提高翻译效果，特别是在高资源语言为其他翻译对提供背景时。

机器翻译中的情境线索：研究多源输入策略在大型语言模型和神经机器翻译系统中的潜力