Towards improved collaborative text editing CRDTs by using Natural Language Processing

Onderzoeksoutput: Conference paper

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Samenvatting

Collaborative text editing systems are used in a variety of cloud-based products. To ensure that documents remain consistent between users, these systems often rely on CRDTs, operational transformation, or other techniques for achieving (strong) eventual consistency. CRDT-based approaches are appealing as they incorporate strategies to ensure that concurrent updates cannot conflict. However, these strategies do not necessarily take into account program semantics and may result in unexpected behaviour from the end-user's perspective.

For example, conflict resolution strategies in collaborative text editors may lead to duplicate words and incorrectly merged sentences. This position paper investigates the use of deterministic natural language processing (NLP) algorithms to improve the concurrency semantics of collaborative text editing systems that rely on CRDTs, aiming to provide a better end-user experience. We explore what is needed to ensure convergence, and highlight potential difficulties with the approach.
Originele taal-2English
TitelPaPoC '23: Proceedings of the 10th Workshop on Principles and Practice of Consistency for Distributed Data
Plaats van productieRome, Italy
UitgeverijACM
Pagina's51-55
Aantal pagina's5
VolumeProceedings of the 10th Workshop on Principles and Practice of Consistency for Distributed Data
Uitgave10th
ISBN van elektronische versie979-8-4007-0086-6
DOI's
StatusPublished - 8 mei 2023
Evenement10th Workshop on Principles and Practice of Consistency for Distributed Data - Rome, Italy
Duur: 8 mei 2023 → …

Publicatie series

NaamPaPoC 2023 - Proceedings of the 10th Workshop on Principles and Practice of Consistency for Distributed Data, Part of: EuroSys 2023

Workshop

Workshop10th Workshop on Principles and Practice of Consistency for Distributed Data
Verkorte titelPaPoC '23
Land/RegioItaly
StadRome
Periode8/05/23 → …

Bibliografische nota

Publisher Copyright:
© 2023 ACM.

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