Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack
Keqing He, Jinchao Zhang, Yuanmeng Yan, Weiran Xu, Cheng Niu, Jie Zhou
Abstract
Zero-shot slot filling has widely arisen to cope with data scarcity in target domains. However, previous approaches often ignore constraints between slot value representation and related slot description representation in the latent space and lack enough model robustness. In this paper, we propose a Contrastive Zero-Shot Learning with Adversarial Attack (CZSL-Adv) method for the cross-domain slot filling. The contrastive loss aims to map slot value contextual representations to the corresponding slot description representations. And we introduce an adversarial attack training strategy to improve model robustness. Experimental results show that our model significantly outperforms state-of-the-art baselines under both zero-shot and few-shot settings.
BibTeX
@inproceedings{he-etal-2020-contrastive,
title = "Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack",
author = "He, Keqing and
Zhang, Jinchao and
Yan, Yuanmeng and
Xu, Weiran and
Niu, Cheng and
Zhou, Jie",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.126/",
doi = "10.18653/v1/2020.coling-main.126",
pages = "1461--1467"
}