  Translating cultural-specific content is crucial for effective cross-cultural
communication. However, many MT systems still struggle to translate sentences
containing cultural-specific entities accurately and understandably. Recent
advancements in in-context learning utilize lightweight prompts to guide large
language models (LLMs) in machine translation tasks. Nevertheless, the
effectiveness of this approach in enhancing machine translation with cultural
awareness remains uncertain. To address this gap, we introduce a new data
curation pipeline to construct a culturally relevant parallel corpus, enriched
with annotations of cultural-specific items. Furthermore, we devise a novel
evaluation metric to assess the understandability of translations in a
reference-free manner by GPT-4. We evaluate a variety of neural machine
translation (NMT) and LLM-based MT systems using our dataset. Additionally, we
propose several prompting strategies for LLMs to incorporate external and
internal cultural knowledge into the translation process. Our results
demonstrate that eliciting explanations can significantly enhance the
understandability of cultural-specific entities, especially those without
well-known translations.
