Interpretation modeling: Social grounding of sentences by reasoning over
  their implicit moral judgments
Liesbeth Allein Maria Mihaela Truşcǎ Marie-Francine Moens
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.