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The social and implicit nature of human communication ramifies readers'understandings of written sentences. Single gold-standard interpretationsrarely exist, challenging conventional assumptions in natural languageprocessing. This work introduces the interpretation modeling (IM) task whichinvolves modeling several interpretations of a sentence's underlying semanticsto unearth layers of implicit meaning. To obtain these, IM is guided bymultiple annotations of social relation and common ground - in this workapproximated by reader attitudes towards the author and their understanding ofmoral judgments subtly embedded in the sentence. We propose a number ofmodeling strategies that rely on one-to-one and one-to-many generation methodsthat take inspiration from the philosophical study of interpretation. Afirst-of-its-kind IM dataset is curated to support experiments and analyses.The modeling results, coupled with scrutiny of the dataset, underline thechallenges of IM as conflicting and complex interpretations are sociallyplausible. This interplay of diverse readings is affirmed by automated andhuman evaluations on the generated interpretations. Finally, toxicity analysesin the generated interpretations demonstrate the importance of IM for refiningfilters of content and assisting content moderators in safeguarding the safetyin online discourse.

Interpretation modeling: Social grounding of sentences by reasoning over their implicit moral judgments