Traditional neural machine translation (NMT) systems often fail to translate
sentences that contain culturally specific information. Most previous NMT
methods have incorporated external cultural knowledge during training, which
requires fine-tuning on low-frequency items specific to the culture. Recent
in-context learning utilizes lightweight prompts to guide large language models
(LLMs) to perform machine translation, however, whether such an approach works
in terms of injecting culture awareness into machine translation remains
unclear. To this end, we introduce a new data curation pipeline to construct a
culturally relevant parallel corpus, enriched with annotations of
cultural-specific entities. Additionally, we design simple but effective
prompting strategies to assist this LLM-based translation. Extensive
experiments show that our approaches can largely help incorporate cultural
knowledge into LLM-based machine translation, outperforming traditional NMT
systems in translating cultural-specific sentences.

本研究着眼于如何将文化知识应用于大型语言模型的机器翻译中，提出了一种新的数据筛选方法来构建具有文化相关性的平行语料库，并设计了简单有效的提示策略以帮助大型语言模型实现机器翻译。经过广泛实验表明，这些方法可以大大帮助将文化知识融入到大型语言模型的机器翻译中，从而在翻译文化特定的句子方面优于传统的 NMT 系统。

基于 LLM 的机器翻译中加入文化意识

Empowering LLM-based Machine Translation with Cultural Awareness

Motifs are distinctive recurring elements found in folklore that have
significance as communicative devices in news, literature, press releases, and
propaganda. Motifs concisely imply a large constellation of culturally-relevant
information, and their broad usage suggests their cognitive importance as
touchstones of cultural knowledge, making their detection a worthy step toward
culturally-aware natural language processing tasks. Until now, folklorists and
others interested in motifs have only extracted motifs from narratives
manually. We present a preliminary report on the development of a system for
automatically detecting motifs. We briefly describe an annotation effort to
produce data for training motif detection, which is on-going. We describe our
in-progress architecture in detail, which aims to capture, in part, how people
determine whether or not a motif candidate is being used in a motific way. This
description includes a test of an off-the-shelf metaphor detector as a feature
for motif detection, which achieves a F1 of 0.35 on motifs and a macro-average
F1 of 0.21 across four categories which we assign to motif candidates.

本文初步报告了一项关于自动检测民间传说中的 Motifs 的系统开发，其中包括用于训练的标记工作、在建构架构中设计用于捕捉 Motifs 的关键特征、和测试案例的测量结果，进一步证明 Motifs 的重要性以及其在文化认知领域自动化处理的潜在价值。