Order-Agnostic Data Augmentation for Few-Shot Named Entity Recognition (2024)

Abstract: 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 order-agnostic data augmentation (OaDA), an alternative solution that exploits the often overlooked order-agnostic property in the training data construction phase of sequence-to-sequence 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 significantly enhance the few-shot capabilities of PLMs with OaDA.

## introduction named entity recognition (ner) @xcite has been one of the most long-standing and fundamental tasks. however, the effectiveness of ner systems is often constrained by the need for substantial high-quality, annotated datasets, which are * work done during the internship at damo academy. ## experiments datasets we conduct comprehensive experiments on two flat ner datasets and one nested ner dataset in several few-shot settings. for flat ner datasets, we choose @xcite and mit-movie @xcite from two different domains. for mit-movie, we randomly select 15% samples from its training set as the development set. for nested ner, we conduct experiments on ace-2005 @xcite , using the same data split as @xcite . ## conclusions in this paper, we propose a novel data augmentation method oada by leveraging the often-overlooked order-agnostic property of ner. furthermore, to jointly utilize the augmented data together without suffering from the one-to-many issue, we introduce the use of ordering instructions and an innovative oada-xe loss, tackling the issue in inter-type and intra-type aspects separately. experiments on three major ner benchmarks, and extensive analyses demonstrate the effectiveness of oada.