论文标题
Adaptkeybert：一种基于注意的方法，用于keybert的几次和零射击域的适应
AdaptKeyBERT: An Attention-Based approach towards Few-Shot & Zero-Shot Domain Adaptation of KeyBERT
论文作者
论文摘要
关键字提取一直是现代自然语言处理的重要主题。其应用程序从本体生成，摘要文本中的事实验证以及推荐系统不等。尽管它具有重要的数据密集型应用程序，但是当数据集很小时，它通常会受到阻碍。关键字提取器的下游培训是一个漫长的过程，需要大量数据。最近，已经提出了很少的学习（FSL）和零射击学习（ZSL）来解决这个问题。因此，我们提出了AdaptKeybert，这是一种通过将正则注意力的概念纳入下游域适应的训练阶段，用于训练使用LLM碱基训练关键字提取器的管道。正如我们认为我们的工作具有含义，要在FSL/ZSL和关键字提取的管道中使用，我们开放代码代码，并在https://github.com/amanpriyanshu/adaptkeybert上提供同名Adaptkeybert的微型库Adaptkeybert。
Keyword extraction has been an important topic for modern natural language processing. With its applications ranging from ontology generation, fact verification in summarized text, and recommendation systems. While it has had significant data-intensive applications, it is often hampered when the data set is small. Downstream training for keyword extractors is a lengthy process and requires a significant amount of data. Recently, Few-shot Learning (FSL) and Zero-Shot Learning (ZSL) have been proposed to tackle this problem. Therefore, we propose AdaptKeyBERT, a pipeline for training keyword extractors with LLM bases by incorporating the concept of regularized attention into a pre-training phase for downstream domain adaptation. As we believe our work has implications to be utilized in the pipeline of FSL/ZSL and keyword extraction, we open-source our code as well as provide the fine-tuning library of the same name AdaptKeyBERT at https://github.com/AmanPriyanshu/AdaptKeyBERT.
