Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition

Yongqi Li, Yu Yu, Tieyun Qian

Submission Type: Regular Long Paper

Submission Track: Information Extraction

Submission Track 2: NLP Applications

Keywords: Named Entity Recognition, Few-Shot Learning

TL;DR: A decomposed framework for solving few-shot named entity recognition.

Abstract:

Despite the recent success achieved by several two-stage prototypical networks in few-shot named entity recognition (NER) task, the over-detected false spans at span detection stage and the inaccurate and unstable prototypes at type classification stage remain to be challenging problems.
In this paper, we propose a novel Type-Aware Decomposed framework, namely TadNER, to solve these problems.
We first present a type-aware span filtering strategy to filter out false spans by removing those semantically far away from type names. We then present a type-aware contrastive learning strategy to construct more accurate and stable prototypes by jointly exploiting support samples and type names as references.
Extensive experiments on various benchmarks prove that our proposed TadNER framework yields a new state-of-the-art performance.

Submission Number: 1228