Fake it Till You Make it：单语词嵌入任务的自我监督语义转换
Fake it Till You Make it: Self-Supervised Semantic Shifts for Monolingual Word Embedding Tasks
摘要：
语言的使用会随着时间的推移以及社会群体和知识领域的变化而变化，甚至在单语情况下也会产生差异。这种词汇使用上的变化通常被称为词汇语义变化（LSC）。LSC的目标是描述和量化语言在词义方面的变化，以衡量两种语言源（即人或语言模型）的区别。由于几乎没有任何数据可用于这样的任务，大多数解决方案都涉及无监督方法来对齐两个嵌入并预测距离度量的语义变化。为此，我们提出了一种自监督的方法来模拟词汇语义变化，通过在输入语料库中引入词向量的扰动来生成训练样本。我们表明，我们的方法可以用于任何对齐方法的语义变化的检测。此外，它可以用来选择在对齐中使用的标志词，并可以导致对现有的对齐技术的实质性改进。我们使用三个不同的数据集上的实验结果来说明我们的技术的实用性，涉及具有相同或不同含义的单词。我们的方法不仅提供了显着的改进，但也可以导致新的发现LSC问题。
英文摘要：
The use of language is subject to variation over time as well as across social groups and knowledge domains, leading to differences even in the monolingual scenario. Such variation in word usage is often called lexical semantic change (LSC). The goal of LSC is to characterize and quantify language variations with respect to word meaning, to measure how distinct two language sources are (that is, people or language models). Because there is hardly any data available for such a task, most solutions involve unsupervised methods to align two embeddings and predict semantic change with respect to a distance measure. To that end, we propose a self-supervised approach to model lexical semantic change by generating training samples by introducing perturbations of word vectors in the input corpora. We show that our method can be used for the detection of semantic change with any alignment method. Furthermore, it can be used to choose the landmark words to use in alignment and can lead to substantial improvements over the existing techniques for alignment. We illustrate the utility of our techniques using experimental results on three different datasets, involving words with the same or different meanings. Our methods not only provide significant improvements but also can lead to novel findings for the LSC problem.
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。
