Phrase representations play an important role in data science and natural language processing, benefiting various tasks like Entity Alignment, Record Linkage, Fuzzy Joins, and Paraphrase Classification. The current state-of-the-art method involves fine-tuning pre-trained language models for phrasal embeddings using contrastive learning. However, we have identified areas for improvement. First, these pre-trained models tend to be unnecessarily complex and require to be pre-trained on a corpus with context sentences. Second, leveraging the phrase type and morphology gives phrase representations that are both more precise and more flexible. We propose an improved framework to learn phrase representations in a context-free fashion. The framework employs phrase type classification as an auxiliary task and incorporates character-level information more effectively into the phrase representation. Furthermore, we design three granularities of data augmentation to increase the diversity of training samples. Our experiments across a wide range of tasks show that our approach generates superior phrase embeddings compared to previous methods while requiring a smaller model size. The code is available at \faGithub~ \url{https://github.com/tigerchen52/PEARL} \end{abstract}

我们提出了一个改进的框架来以无上下文的方式学习短语表示, 该框架使用短语类型分类作为辅助任务, 并更有效地将字符级信息融入短语表示, 通过设计三种不同程度的数据增强来增加训练样本的多样性, 我们的实验结果表明, 相对于先前的方法, 我们的方法生成了更优秀的短语嵌入, 同时还需要更小的模型大小。

学习高品质和通用性的短语表示