Computational Models of Anaphora

Interpreting anaphoric references is a fundamental aspect of our language competence that has long attracted the attention of computational linguists. The appearance of ever-larger anaphorically annotated data sets covering more and more anaphoric phenomena in ever-greater detail has spurred the development of increasingly more sophisticated computational models; as a result, the most recent state-of-the-art neural models are able to achieve impressive performance by leveraging linguistic, lexical, discourse, and encyclopedic information. This article provides a thorough survey of anaphora resolution (coreference) throughout this development, reviewing the available data sets and covering both the preneural history of the field and-in more detail-current neural models, including research on less-studied aspects of anaphoric interpretation such as bridging reference resolution and discourse deixis interpretation.