A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NER

• URL: http://arxiv.org/abs/2308.14533v1
• Date: Mon, 28 Aug 2023 12:46:21 GMT
• Title: A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NER
• Authors: Guanting Dong, Zechen Wang, Jinxu Zhao, Gang Zhao, Daichi Guo, Dayuan Fu, Tingfeng Hui, Chen Zeng, Keqing He, Xuefeng Li, Liwen Wang, Xinyue Cui, Weiran Xu
• Abstract summary: We propose a Multi-Task Semantic Decomposition Framework via Joint Task-specific Pre-training for few-shot NER. We introduce two novel pre-training tasks: Demonstration-based Masked Language Modeling (MLM) and Class Contrastive Discrimination. In the downstream main task, we introduce a multi-task joint optimization framework with the semantic decomposing method, which facilitates the model to integrate two different semantic information for entity classification.
• Score: 26.008350261239617
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The objective of few-shot named entity recognition is to identify named entities with limited labeled instances. Previous works have primarily focused on optimizing the traditional token-wise classification framework, while neglecting the exploration of information based on NER data characteristics. To address this issue, we propose a Multi-Task Semantic Decomposition Framework via Joint Task-specific Pre-training (MSDP) for few-shot NER. Drawing inspiration from demonstration-based and contrastive learning, we introduce two novel pre-training tasks: Demonstration-based Masked Language Modeling (MLM) and Class Contrastive Discrimination. These tasks effectively incorporate entity boundary information and enhance entity representation in Pre-trained Language Models (PLMs). In the downstream main task, we introduce a multi-task joint optimization framework with the semantic decomposing method, which facilitates the model to integrate two different semantic information for entity classification. Experimental results of two few-shot NER benchmarks demonstrate that MSDP consistently outperforms strong baselines by a large margin. Extensive analyses validate the effectiveness and generalization of MSDP.

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