Order-Agnostic Data Augmentation for Few-Shot Named Entity Recognition
Hui‐Ming Wang, Liying Cheng, Wenxuan Zhang, De Wen Soh, Lidong Bing · 2024
Data augmentation (DA) methods have been proven to be effective for pre-trained language models (PLMs) in low-resource settings, including few-shot named entity recognition (NER).However, existing NER DA techniques either perform rule-based manipulations on words that break the semantic coherence of the sentence, or exploit generative models for entity or context substitution, which requires a substantial amount of labeled data and contradicts the objective of operating in low-resource settings.In this work, we propose orderagnostic data augmentation (OADA), an alternative solution that exploits the often overlooked order-agnostic property in the training data construction phase of sequence-tosequence NER methods for data augmentation.To effectively utilize the augmented data without suffering from the one-to-many issue, where multiple augmented target sequences exist for one single sentence, we further propose the use of ordering instructions and an innovative OADA-XE loss.Specifically, by treating each permutation of entity types as an ordering instruction, we rearrange the entity set accordingly, ensuring a distinct input-output pair, while OADA-XE assigns loss based on the best match between the target sequence and model predictions.We conduct comprehensive experiments and analyses across three major NER benchmarks and can significantly enhance the few-shot capabilities of PLMs with OADA.Our code is available at https://github.com/Circle-Ming/OADA-NER.