5 citations · 7 across the 7 of their papers we have counts for
7 papers
A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NER
Guanting Dong, Zechen Wang, Jinxu Zhao +10
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…
Generative Zero-Shot Prompt Learning for Cross-Domain Slot Filling with Inverse Prompting
Xuefeng Li, Liwen Wang, Guanting Dong +5
Zero-shot cross-domain slot filling aims to transfer knowledge from the labeled source domain to the unlabeled target domain. Existing models either encode slot descriptions and ex…
Revisit Out-Of-Vocabulary Problem for Slot Filling: A Unified Contrastive Frameword with Multi-level Data Augmentations
Daichi Guo, Guanting Dong, Dayuan Fu +9
In real dialogue scenarios, the existing slot filling model, which tends to memorize entity patterns, has a significantly reduced generalization facing Out-of-Vocabulary (OOV) prob…
A Prototypical Semantic Decoupling Method via Joint Contrastive Learning for Few-Shot Name Entity Recognition
Guanting Dong, Zechen Wang, Liwen Wang +10
Few-shot named entity recognition (NER) aims at identifying named entities based on only few labeled instances. Most existing prototype-based sequence labeling models tend to memor…
PSSAT: A Perturbed Semantic Structure Awareness Transferring Method for Perturbation-Robust Slot Filling
Guanting Dong, Daichi Guo, Liwen Wang +10
Most existing slot filling models tend to memorize inherent patterns of entities and corresponding contexts from training data. However, these models can lead to system failure or…
A Robust Contrastive Alignment Method For Multi-Domain Text Classification
Xuefeng Li, Hao Lei, Liwen Wang +5
Multi-domain text classification can automatically classify texts in various scenarios. Due to the diversity of human languages, texts with the same label in different domains may…
Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot Filling
Liwen Wang, Xuefeng Li, Jiachi Liu +3
Zero-shot cross-domain slot filling alleviates the data dependence in the case of data scarcity in the target domain, which has aroused extensive research. However, as most of the…