TECA: A Two-stage Approach with Controllable Attention Soft Prompt for Few-shot Nested Named Entity Recognition

Abstract

Few-shot nested named entity recognition (NER), identifying named entities that are nested with a small number of labeled data, has attracted much attention. Recently, a span-based method based on three stages ( focusing, bridging and prompting) has been proposed for few-shot nested NER. However, such a span-based approach for few-shot nested NER suffers from two challenges: 1) error propagation because of its 3-stage pipeline-based framework; 2) ignoring the relationship between inner and outer entities, which is crucial for few-shot nested NER. Therefore, in this work, we propose a two-stage approach with a controllable attention soft prompt for few-shot nested named entity recognition (TECA). It consists of two components: span part identification and entity mention recognition. The span part identification provides possible entity mentions without an extra filtering module. The entity mention recognition pays fine-grained attention to the inner and outer entities and the corresponding adjacent context through the controllable attention soft prompt to classify the candidate entity mentions. Experimental results show that the TECA approach achieves state-of-the-art performance consistently on the four benchmark datasets (ACE2004, ACE2005, GENIA, and KBP2017) and outperforms several competing baseline models on F1-score by 5.62% on ACE04, 5.11% on ACE05, 3.41% on KBP2017 and 0.7% on GENIA on the 10-shot setting.