  Complex Named Entity Recognition (NER) is the task of detecting
linguistically complex named entities in low-context text. In this paper, we
present ACLM Attention-map aware keyword selection for Conditional Language
Model fine-tuning), a novel data augmentation approach based on conditional
generation to address the data scarcity problem in low-resource complex NER.
ACLM alleviates the context-entity mismatch issue, a problem existing NER data
augmentation techniques suffer from and often generates incoherent
augmentations by placing complex named entities in the wrong context. ACLM
builds on BART and is optimized on a novel text reconstruction or denoising
task - we use selective masking (aided by attention maps) to retain the named
entities and certain keywords in the input sentence that provide contextually
relevant additional knowledge or hints about the named entities. Compared with
other data augmentation strategies, ACLM can generate more diverse and coherent
augmentations preserving the true word sense of complex entities in the
sentence. We demonstrate the effectiveness of ACLM both qualitatively and
quantitatively on monolingual, cross-lingual, and multilingual complex NER
across various low-resource settings. ACLM outperforms all our neural baselines
by a significant margin (1%-36%). In addition, we demonstrate the application
of ACLM to other domains that suffer from data scarcity (e.g., biomedical). In
practice, ACLM generates more effective and factual augmentations for these
domains than prior methods. Code: https://github.com/Sreyan88/ACLM
