EPT: Data Augmentation with Embedded Prompt Tuning for Low-Resource Named Entity Recognition
Year: 2023
Type: article
Abstract: Data augmentation methods are often used to address data scarcity in natural language processing (NLP). However, token-label misalignment, which refers to situations where tokens are matched with incorrect entity labels in the augmented sentences, hinders the data augmentation methods from achieving high scores in token-level tasks like named entity recognition (NER). In this paper, we propose embedded prompt tuning (EPT) as a novel data augmentation approach to low-resource NER. To address the problem of token-label misalignment, we implicitly embed NER labels as prompt into the hidden layer of pre-trained language model, and therefore entity tokens masked can be predicted by the finetuned EPT. Hence, EPT can generate high-quality and high-diverse data with various entities, which improves performance of NER. As datasets of cross-domain NER are available, we also explore NER domain adaption with EPT. The experimental results show that EPT achieves substantial improvement over the baseline methods on low-resource NER tasks.
Authors: Hongfei YU, Kunyu NI, Rongkang XU, Wenjun YU, Yu HUANG
Institution: Huazhong Agricultural University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4386486129