Revisiting Memory-Efficient Incremental Coreference Resolution

04/30/2020
by   Patrick Xia, et al.
0

We explore the task of coreference resolution under fixed memory by extending an incremental clustering algorithm to utilize contextualized encoders and neural components. Our algorithm creates explicit representations for each entity, where given a new sentence, spans are proposed and subsequently scored against each entity representation, leading to emergent clusters. Our approach is end-to-end trainable and can be used to transform existing models, leading to an asymptotic reduction in memory usage while remaining competitive on task performance, which allows for more efficient use of computational resources for short documents, and making coreference more feasible across very long document contexts.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/06/2020

Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks

Long document coreference resolution remains a challenging task due to t...
research
06/02/2021

Cross-document Coreference Resolution over Predicted Mentions

Coreference resolution has been mostly investigated within a single docu...
research
05/26/2023

Sentence-Incremental Neural Coreference Resolution

We propose a sentence-incremental neural coreference resolution system w...
research
01/04/2021

Are Eliminated Spans Useless for Coreference Resolution? Not at all

Various neural-based methods have been proposed so far for joint mention...
research
02/03/2022

Towards Coherent and Consistent Use of Entities in Narrative Generation

Large pre-trained language models (LMs) have demonstrated impressive cap...
research
05/12/2021

Graph Neural Networks for Inconsistent Cluster Detection in Incremental Entity Resolution

Online stores often utilize product relationships such as bundles and su...
research
02/05/2019

The Referential Reader: A Recurrent Entity Network for Anaphora Resolution

We present a new architecture for storing and accessing entity mentions ...

Please sign up or login with your details

Forgot password? Click here to reset