The social and implicit nature of human communication ramifies readers' understandings of written sentences. Single gold-standard interpretations rarely exist, challenging conventional assumptions in natural language processing. This work introduces the interpretation modeling (IM) task which involves modeling several interpretations of a sentence's underlying semantics to unearth layers of implicit meaning. To obtain these, IM is guided by multiple annotations of social relation and common ground - in this work approximated by reader attitudes towards the author and their understanding of moral judgments subtly embedded in the sentence. We propose a number of modeling strategies that rely on one-to-one and one-to-many generation methods that take inspiration from the philosophical study of interpretation. A first-of-its-kind IM dataset is curated to support experiments and analyses. The modeling results, coupled with scrutiny of the dataset, underline the challenges of IM as conflicting and complex interpretations are socially plausible. This interplay of diverse readings is affirmed by automated and human evaluations on the generated interpretations. Finally, toxicity analyses in the generated interpretations demonstrate the importance of IM for refining filters of content and assisting content moderators in safeguarding the safety in online discourse.

该论文介绍了一项名为“解释建模（IM）”的任务，旨在通过建模被一句话潜在的语义的多个解释来揭示其隐含意义的层次结构。通过读者对作者态度和对嵌入在句子中的道德判断的理解的多个标注来指导IM，并提出了一些建模策略，这些策略受到解释学的启发。同时，该论文还创建了一个IM数据集，支持实验和分析，并通过自动化和人工评估验证了多样化解读的挑战以及生成的解释在内容安全上的重要性。

解释建模：通过推理引出句子的隐含道德判断