We Hebben Een Serieus Translatie: Modeling Intercomprehension as Probabilistic Inference

• URL: http://arxiv.org/abs/2607.12169v1
• Date: Mon, 13 Jul 2026 21:30:05 GMT
• Title: We Hebben Een Serieus Translatie: Modeling Intercomprehension as Probabilistic Inference
• Abstract summary: Intercomprehension refers to partial intelligibility of an unfamiliar language by a speaker of a related language.<n>In this work, we extend past work on algorithmic models of noisy-channel inference to model intercomprehension in a Bayesian framework.<n>We conduct a human behavioral experiment, eliciting inferences for utterances in Dutch, Italian, and Ukrainian from speakers of English, Spanish, and Russian, respectively.
• Score: 10.293916944634804
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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