[Short paper] Towards improved collaborative text editing CRDTs by using Natural Language Processing

Jim Bauwens, Kevin De Porre, Elisa Gonzalez Boix
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Abstract

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.
[短文]利用自然语言处理改进协同文本编辑crdt
协作文本编辑系统用于各种基于云的产品。为了确保文档在用户之间保持一致,这些系统通常依赖于crdt、操作转换或其他技术来实现(强)最终的一致性。基于crdt的方法很有吸引力,因为它们包含了确保并发更新不会冲突的策略。然而,这些策略不一定考虑到程序语义,从最终用户的角度来看可能会导致意外的行为。例如,协作文本编辑器中的冲突解决策略可能导致重复的单词和错误合并的句子。本文研究了确定性自然语言处理(NLP)算法的使用,以改进依赖于crdt的协作文本编辑系统的并发语义,旨在提供更好的最终用户体验。我们探讨了确保趋同所需的条件,并强调了这种方法的潜在困难。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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