用概率推理模拟跨语言理解，解释母语者如何听懂相关陌生语言。
We Hebben Een Serieus Translatie: Modeling Intercomprehension as Probabilistic Inference
- 基于贝叶斯框架，仅用母语语言模型评分潜在翻译假设。
- 实验显示模型预测人类理解表现的准确性优于消融版本和大模型零样本提示。
- 适合对认知计算模型、多语言理解感兴趣的研究者。
跨理解指母语者（L1）在不熟悉目标语言（L2）的情况下，仍能部分理解其内容。本研究将算法化的噪声信道推断扩展至贝叶斯框架，构建一个仅依赖L1语言模型评分潜在翻译假设的模型，并利用通用噪声模型基于形式相似性或符号规则推断L2与L1词间的映射。我们开展人类行为实验，分别向英语、西班牙语和俄语母语者呈现荷兰语、意大利语和乌克兰语的语句，收集其理解推断。完整模型在拟合人类跨理解表现分布上优于各消融版本，且在零样本条件下优于大规模模型的提示方法。结果为跨理解提供了具认知合理性、灵活应对现实不确定性场景的计算模型。代码已公开。
原文摘要 · Abstract (English)
Intercomprehension refers to partial intelligibility of an unfamiliar language (L2) by a speaker of a related language (L1). How is this zero-shot cross-language comprehension possible? In this work, we extend past work on algorithmic models of noisy-channel inference to model intercomprehension in a Bayesian framework. The model uses an LM in L1 only for scoring latent hypotheses about the translations of observed L2 utterances, and a general-purpose noise model to infer a mapping between L2 and L1 words based on either form-based similarity or symbolic rules. We then conduct a human behavioral experiment, eliciting inferences for utterances in Dutch, Italian, and Ukrainian from speakers of English, Spanish, and Russian, respectively. Our full model shows a closer alignment to the distribution of human intercomprehension performance than ablations, and also compares favorably to zero-shot prompting of much larger models. These results provide a cognitively plausible computational model of intercomprehension, and highlight the flexible inferences made by comprehenders under wide uncertainty in real-world cross-language scenarios. We share our code publicly.
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