Representation Learning for Improved Generalization of Adversarial Domain Adaptation with Text Classification

Khaddaj, Alaa; Hajj, Hazem

doi:10.1109/iciot48696.2020.9089430

Cited by 2 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, we aim to directly reuse or fine-tune some existing representation in transfer learning, while a meta-learner is typically optimized at adapting to new tasks. Domain adaptation (Ganin et al, 2016;Tzeng et al, 2017;Khaddaj and Hajj, 2020) is a type of transfer learning, which aims to bridge the gap between the source and target domains by learning domain-invariant feature representations. Pre-trained model (Devlin et al, 2019;Yang et al, 2019;Brown et al, 2020) can also be viewed as a type of transfer learning.…”

Section: Related Workmentioning

confidence: 99%

Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification

Han¹,

Fan²,

Zhang³

et al. 2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

View full text Add to dashboard Cite

Meta-learning has emerged as a trending technique to tackle few-shot text classification and achieved state-of-the-art performance. However, existing solutions heavily rely on the exploitation of lexical features and their distributional signatures on training data, while neglecting to strengthen the model's ability to adapt to new tasks. In this paper, we propose a novel meta-learning framework integrated with an adversarial domain adaptation network, aiming to improve the adaptive ability of the model and generate high-quality text embedding for new classes. Extensive experiments are conducted on four benchmark datasets and our method demonstrates clear superiority over the state-of-the-art models in all the datasets. In particular, the accuracy of 1shot and 5-shot classification on the dataset of 20 Newsgroups is boosted from 52.1% to 59.6%, and from 68.3% to 77.8%, respectively 1 .

show abstract