An Enhanced Span-based Decomposition Method for Few-Shot Sequence
Labeling
- URL: http://arxiv.org/abs/2109.13023v1
- Date: Mon, 27 Sep 2021 12:59:48 GMT
- Title: An Enhanced Span-based Decomposition Method for Few-Shot Sequence
Labeling
- Authors: Peiyi Wang, Runxin Xu, Tianyu Liu, Qingyu Zhou, Yunbo Cao, Baobao
Chang, Zhifang Sui
- Abstract summary: Few-Shot Sequence Labeling (FSSL) is a canonical solution for the tagging models to generalize on an emerging, resource-scarce domain.
We propose Enhanced Span-based Decomposition method, which follows the metric-based meta-learning paradigm for FSSL.
- Score: 27.468499201647063
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Few-Shot Sequence Labeling (FSSL) is a canonical solution for the tagging
models to generalize on an emerging, resource-scarce domain. In this paper, we
propose ESD, an Enhanced Span-based Decomposition method, which follows the
metric-based meta-learning paradigm for FSSL. ESD improves previous methods
from two perspectives: a) Introducing an optimal span decomposition framework.
We formulate FSSL as an optimization problem that seeks for an optimal span
matching between test query and supporting instances. During inference, we
propose a post-processing algorithm to alleviate false positive labeling by
resolving span conflicts. b) Enhancing representation for spans and class
prototypes. We refine span representation by inter- and cross-span attention,
and obtain the class prototypical representation with multi-instance learning.
To avoid the semantic drift when representing the O-type (not a specific entity
or slot) prototypes, we divide the O-type spans into three categories according
to their boundary information. ESD outperforms previous methods in two popular
FSSL benchmarks, FewNERD and SNIPS, and is proven to be more robust in the
nested and noisy tagging scenarios.
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