为神圣之物建模：在自然语言处理中使用宗教文本时的考量
Modeling the Sacred: Considerations when Using Religious Texts in Natural Language Processing
AI总结：
本文从 NLP 伦理出发，审视宗教文本及其译本在低资源语言研究中的再利用，强调除模型偏见外还须关注数据来源、文化语境、传教意图及边缘化社群视角。
摘要：
本立场论文关注宗教文本在自然语言处理（NLP）中的使用，这一议题对 NLP 伦理学具有特殊意义。宗教文本是具有重要文化价值的表达，而机器学习模型倾向于复现其训练数据中编码的文化价值。此外，当语言数据稀缺时，NLP 研究人员经常使用宗教文本的译本。这使这些译本脱离了其原始用途和动机，而这些用途和动机往往涉及吸引新的信徒。本文认为，NLP 对此类文本的使用引发了超越模型偏见的诸多考量，包括数据来源、文化背景，以及它们在传教活动中的用途。我们主张应更多考量研究者的立场性，以及边缘化语言群体和宗教群体的视角。
英文摘要：
This position paper concerns the use of religious texts in Natural Language Processing (NLP), which is of special interest to the Ethics of NLP. Religious texts are expressions of culturally important values, and machine learned models have a propensity to reproduce cultural values encoded in their training data. Furthermore, translations of religious texts are frequently used by NLP researchers when language data is scarce. This repurposes the translations from their original uses and motivations, which often involve attracting new followers. This paper argues that NLP's use of such texts raises considerations that go beyond model biases, including data provenance, cultural contexts, and their use in proselytism. We argue for more consideration of researcher positionality, and of the perspectives of marginalized linguistic and religious communities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
