The Referential Reader: A RecurrentEntity Network for Anaphora Resolution

Published:

Fei Liu, Luke Zettlemoyer and Jacob Eisenstein (to appear) The Referential Reader: A RecurrentEntity Network for Anaphora Resolution, In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy.

@InProceedings{Liu+:2019,
  author    = {Liu, Fei  and  Zettlemoyer, Luke and Jacob, Eisenstein},
  title     = {The Referential Reader: A RecurrentEntity Network for Anaphora Resolution},
  booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
  year      = {2019},
  address   = {Florence, Italy}
}

Abstract

We present a new architecture for storing and accessing entity mentions during online text processing. While reading the text, entity references are identified, and may be stored by either updating or overwriting a cell in a fixed-length memory. The update operation implies coreference with the other mentions that are stored in the same cell; the overwrite operation causes these mentions to be forgotten. By encoding the memory operations as differentiable gates, it is possible to train the model end-to-end, using both a supervised anaphora resolution objective as well as a supplementary language modeling objective. Evaluation on a dataset of pronoun-name anaphora demonstrates strong performance with purely incremental text processing.