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