Year: 2019
Type: conference-paper
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 fixedlength 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.
DOI: 10.18653/v1/p19-1593
Authors: Fei Liu, Luke Zettlemoyer, Jacob Eisenstein
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2964093485