Contextual Cues in Machine Translation: Investigating the Potential of Multi-Source Input Strategies in LLMs and NMT Systems

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.

Defining Boundaries: The Impact of Domain Specification on Cross-Language and Cross-Domain Transfer in Machine Translation

Recent advancements in neural machine translation (NMT) have revolutionized the field, yet the dependency on extensive parallel corpora limits progress for low-resource languages. Cross-lingual transfer learning offers a promising solution by utilizing data from high-resource languages but often struggles with in-domain NMT. In this paper, we investigate three pivotal aspects: enhancing the domain-specific quality of NMT by fine-tuning domain-relevant data from different language pairs, identifying which domains are transferable in zero-shot scenarios, and assessing the impact of language-specific versus domain-specific factors on adaptation effectiveness. Using English as the source language and Spanish for fine-tuning, we evaluate multiple target languages including Portuguese, Italian, French, Czech, Polish, and Greek. Our findings reveal significant improvements in domain-specific translation quality, especially in specialized fields such as medical, legal, and IT, underscoring the importance of well-defined domain data and transparency of the experiment setup in in-domain transfer learning.