DaMSTF: Domain Adversarial Learning Enhanced Meta Self-Training for
  Domain Adaptation
Menglong Lu Zhen Huang Yunxiang Zhao Zhiliang Tian Yang Liu Dongsheng Li
Self-training emerges as an important research line on domain adaptation. By
taking the model's prediction as the pseudo labels of the unlabeled data,
self-training bootstraps the model with pseudo instances in the target domain.
However, the prediction errors of pseudo labels (label noise) challenge the
performance of self-training. To address this problem, previous approaches only
use reliable pseudo instances, i.e., pseudo instances with high prediction
confidence, to retrain the model. Although these strategies effectively reduce
the label noise, they are prone to miss the hard examples. In this paper, we
propose a new self-training framework for domain adaptation, namely Domain
adversarial learning enhanced Self-Training Framework (DaMSTF). Firstly, DaMSTF
involves meta-learning to estimate the importance of each pseudo instance, so
as to simultaneously reduce the label noise and preserve hard examples.
Secondly, we design a meta constructor for constructing the meta-validation
set, which guarantees the effectiveness of the meta-learning module by
improving the quality of the meta-validation set. Thirdly, we find that the
meta-learning module suffers from the training guidance vanishment and tends to
converge to an inferior optimal. To this end, we employ domain adversarial
learning as a heuristic neural network initialization method, which can help
the meta-learning module converge to a better optimal. Theoretically and
experimentally, we demonstrate the effectiveness of the proposed DaMSTF. On the
cross-domain sentiment classification task, DaMSTF improves the performance of
BERT with an average of nearly 4%.