Meta-embedding (ME) learning is an emerging approach that attempts to learn more accurate word embeddings given existing (source) word embeddings as the sole input. Due to their ability to incorporate semantics from multiple source embeddings in a compact manner with superior performance, ME learning has gained popularity among practitioners in NLP. To the best of our knowledge, there exist no prior systematic survey on ME learning and this paper attempts to fill this need. We classify ME learning methods according to multiple factors such as whether they (a) operate on static or contextualised embeddings, (b) trained in an unsupervised manner or (c) fine-tuned for a particular task/domain. Moreover, we discuss the limitations of existing ME learning methods and highlight potential future research directions.

翻译：元构件(ME)学习是一种新兴方法,试图学习更准确的字嵌入,因为现有的(源)字嵌入是唯一的投入。由于它们能够以精良性表现将多种来源的语义嵌入与优异性密切结合的方式纳入多种来源的语义嵌入中,ME学习在NLP的实践者中越来越受欢迎。据我们所知,事先没有关于ME学习的系统调查,本文试图满足这一需要。我们根据多种因素对ME学习方法进行分类,如(a) 静态或背景化嵌入,(b) 以不受监督的方式培训,(c) 微调特定任务/领域。此外,我们讨论了现有ME学习方法的局限性,并突出强调了未来可能的研究方向。