A representation learning method is considered stable if it consistently generates similar representation of the given data across multiple runs. Word Embedding Methods (WEMs) are a class of representation learning methods that generate dense vector representation for each word in the given text data. The central idea of this paper is to explore the stability measurement of WEMs using intrinsic evaluation based on word similarity. We experiment with three popular WEMs: Word2Vec, GloVe, and fastText. For stability measurement, we investigate the effect of five parameters involved in training these models. We perform experiments using four real-world datasets from different domains: Wikipedia, News, Song lyrics, and European parliament proceedings. We also observe the effect of WEM stability on three downstream tasks: Clustering, POS tagging, and Fairness evaluation. Our experiments indicate that amongst the three WEMs, fastText is the most stable, followed by GloVe and Word2Vec.

翻译：若一种表示学习方法能在多次运行中为给定数据生成相似的表示，则称其具有稳定性。词嵌入方法（WEMs）是一类表示学习方法，可为给定文本数据中的每个词生成稠密向量表示。本文的核心思想是基于词语相似度的内在评估，探索词嵌入方法的稳定性度量。我们对三种流行的词嵌入方法进行了实验：Word2Vec、GloVe 和 fastText。在稳定性度量方面，我们研究了训练这些模型时涉及的五个参数的影响。实验使用了来自四个不同领域的真实数据集：维基百科、新闻、歌词以及欧洲议会议事录。我们还观察了词嵌入方法稳定性对三个下游任务的影响：聚类、词性标注和公平性评估。实验结果表明，在这三种词嵌入方法中，fastText 最为稳定，其次是 GloVe 和 Word2Vec。