DeepAI AI Chat
Log In Sign Up

Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network

by   Yutai Hou, et al.

In this paper, we explore the slot tagging with only a few labeled support sentences (a.k.a. few-shot). Few-shot slot tagging faces a unique challenge compared to the other few-shot classification problems as it calls for modeling the dependencies between labels. But it is hard to apply previously learned label dependencies to an unseen domain, due to the discrepancy of label sets. To tackle this, we introduce a collapsed dependency transfer mechanism into the conditional random field (CRF) to transfer abstract label dependency patterns as transition scores. In the few-shot setting, the emission score of CRF can be calculated as a word's similarity to the representation of each label. To calculate such similarity, we propose a Label-enhanced Task-Adaptive Projection Network (L-TapNet) based on the state-of-the-art few-shot classification model – TapNet, by leveraging label name semantics in representing labels. Experimental results show that our model significantly outperforms the strongest few-shot learning baseline by 14.64 F1 scores in the one-shot setting.


page 1

page 2

page 3

page 4


Few-Shot Sequence Labeling with Label Dependency Transfer

Few-shot sequence labeling faces a unique challenge compared with the ot...

Few-Shot Event Detection with Prototypical Amortized Conditional Random Field

Event Detection, a fundamental task of Information Extraction, tends to ...

Inverse is Better! Fast and Accurate Prompt for Few-shot Slot Tagging

Prompting methods recently achieve impressive success in few-shot learni...

Few-Shot NLU with Vector Projection Distance and Abstract Triangular CRF

Data sparsity problem is a key challenge of Natural Language Understandi...

GEDIT: Geographic-Enhanced and Dependency-Guided Tagging for Joint POI and Accessibility Extraction at Baidu Maps

Providing timely accessibility reminders of a point-of-interest (POI) pl...

Using dependency parsing for few-shot learning in distributional semantics

In this work, we explore the novel idea of employing dependency parsing ...

Few-shot Learning for Slot Tagging with Attentive Relational Network

Metric-based learning is a well-known family of methods for few-shot lea...