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.1

中文翻译：

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解释建模：通过对句子的隐含道德判断进行推理来建立句子的社会基础


人类交流的社交性和隐含性影响了读者对书面句子的理解。单一的黄金标准解释很少存在，这挑战了自然语言处理中的传统假设。这项工作介绍了解释建模 （IM） 任务，该任务涉及对句子潜在语义的多种解释进行建模，以挖掘隐含含义的层次。为了获得这些，IM 以社会关系和共同点的多个注释为指导——在这项工作中，读者对作者的态度以及他们对巧妙嵌入句子中的道德判断的理解近似于此。我们提出了许多建模策略，这些策略依赖于一对一和一对多生成方法，这些方法从解释的哲学研究中汲取灵感。精心策划了首个 IM 数据集，以支持实验和分析。建模结果，加上对数据集的审查，强调了 IM 的挑战，因为相互冲突和复杂的解释在社会上是合理的。这种不同阅读的相互作用通过对生成的解释的自动和人工评估得到证实。最后，生成的解释中的毒性分析证明了 IM 对于优化内容过滤器和协助内容审核员维护在线话语安全的重要性。