基于变分推理的自然语言编辑表示学习
Variational Inference for Learning Representations of Natural Language Edits
摘要：
文件编辑已成为信息生产的一个普遍组成部分，版本控制系统使编辑内容能够有效地存储和应用。鉴于此，最近已经提出了学习编辑的分布式表示的任务。考虑到这一点，我们提出了一种新的方法，采用变分推理来学习向量表示的连续潜在空间，以捕获文档编辑过程中的潜在语义信息。我们通过引入一个潜在变量来明确地对上述特征进行建模来实现这一点。然后，该潜在变量与文档表示相结合，以指导生成该文档的编辑版本。此外，为了促进标准化的自动评价的编辑表示，这在很大程度上依赖于直接的人类输入到目前为止，我们还提出了一套下游的任务，PEER，专门设计来衡量质量的编辑表示的背景下，自然语言处理。
英文摘要：
Document editing has become a pervasive component of the production of information, with version control systems enabling edits to be efficiently stored and applied. In light of this, the task of learning distributed representations of edits has been recently proposed. With this in mind, we propose a novel approach that employs variational inference to learn a continuous latent space of vector representations to capture the underlying semantic information with regard to the document editing process. We achieve this by introducing a latent variable to explicitly model the aforementioned features. This latent variable is then combined with a document representation to guide the generation of an edited version of this document. Additionally, to facilitate standardized automatic evaluation of edit representations, which has heavily relied on direct human input thus far, we also propose a suite of downstream tasks, PEER, specifically designed to measure the quality of edit representations in the context of natural language processing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
