翻译不确定性及其分布谬误
Translation Indeterminacy and the Distributional Fallacy
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中文总结 AI 辅助
本文批判分布假设的因果倒置，提出翻译可仅凭跨语言分布对应关系成功，并基于生态-生成主义论证指称与意义需通过智能体-环境交互获得。
中文摘要 AI 辅助
大型语言模型（LLMs）通常与分布假设相关联，根据该假设，（1）语义意义植根于语言语境的分布模式，（2）对跨语言分布对应关系的了解使得成功翻译成为可能。本文否定了第一个主张，认为这是一种因果倒置：语言分布反映的是由意义实践所产生的模式，而非构成其来源。与此同时，本文接受第二个主张，认为翻译——无论是人工翻译还是机器翻译——可以在无需获取意义或指称的情况下成功。对跨语言分布对应关系及其推理组织的了解可能足以完成翻译。本文提出了一种生态-生成主义视角，根据该视角，指称和意义植根于智能体与环境的交互，并通过基于行动的概念（即当前LLMs不具备的世界参与认知形式）得以稳定。
英文摘要
Large language models (LLMs) are commonly associated with the distributional hypothesis, according to which (1) semantic meaning is grounded in distributional patterns of linguistic context, and (2) knowledge of cross-linguistic distributional correspondences allows for successful translation. This paper rejects the first claim as a causal inversion: linguistic distributions reflect patterns arising from meaning-making practices rather than constituting their source. At the same time, it accepts the second claim, arguing that translation -human or machine - can succeed without requiring access to meaning or reference. Knowledge of interlingual distributional correspondence and their inferential organization may be sufficient for translation. The paper develops an ecological-enactivist perspective, according to which reference and meaning are grounded in agent-environment interaction and stabilized through action-grounded concepts, forms of world-involving cognition that current LLMs do not possess.
发表机构
- Kent State University（肯特州立大学）
机构由 AI 辅助整理，请以论文原文为准。
