arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2103.07052cs.CLcs.IRcs.LG
收藏

基于语言分歧的作者身份验证

Improving Authorship Verification using Linguistic Divergence

University of Houston(休斯顿大学)

Yifan Zhang, Dainis Boumber, Marjan Hosseinia, Fan Yang, Arjun Mukherjee

摘要:

我们提出了一个无监督的解决方案,作者身份验证任务,利用预先训练的深度语言模型计算一个新的度量称为DV距离。所提出的度量标准是两个作者之间的差异与预先训练的语言模型进行比较的度量。我们的设计解决了作者身份验证中的非可比性问题,经常遇到的小型或跨域语料库。据我们所知,本文是第一个介绍一种方法,设计与非可比性铭记从地面上,而不是间接。它也是第一个在这种环境中使用深度语言模型的语言。该方法是直观的,并且通过可视化很容易理解和解释。在四个数据集上的实验表明,我们的方法在大多数任务中匹配或超过了当前最先进的强基线。

英文摘要:

We propose an unsupervised solution to the Authorship Verification task that utilizes pre-trained deep language models to compute a new metric called DV-Distance. The proposed metric is a measure of the difference between the two authors comparing against pre-trained language models. Our design addresses the problem of non-comparability in authorship verification, frequently encountered in small or cross-domain corpora. To the best of our knowledge, this paper is the first one to introduce a method designed with non-comparability in mind from the ground up, rather than indirectly. It is also one of the first to use Deep Language Models in this setting. The approach is intuitive, and it is easy to understand and interpret through visualization. Experiments on four datasets show our methods matching or surpassing current state-of-the-art and strong baselines in most tasks.

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。

↑