Domain shift is a big challenge in NLP, thus, many approaches resort to
learning domain-invariant features to mitigate the inference phase domain
shift. Such methods, however, fail to leverage the domain-specific nuances
relevant to the task at hand. To avoid such drawbacks, domain counterfactual
generation aims to transform a text from the source domain to a given target
domain. However, due to the limited availability of data, such frequency-based
methods often miss and lead to some valid and spurious domain-token
associations. Hence, we employ a three-step domain obfuscation approach that
involves frequency and attention norm-based masking, to mask domain-specific
cues, and unmasking to regain the domain generic context. Our experiments
empirically show that the counterfactual samples sourced from our masked text
lead to improved domain transfer on 10 out of 12 domain sentiment
classification settings, with an average of 2% accuracy improvement over the
state-of-the-art for unsupervised domain adaptation (UDA). Further, our model
outperforms the state-of-the-art by achieving 1.4% average accuracy improvement
in the adversarial domain adaptation (ADA) setting. Moreover, our model also
shows its domain adaptation efficacy on a large multi-domain intent
classification dataset where it attains state-of-the-art results. We release
the codes publicly at https://github.com/declare-lab/remask.

本文提出了一种三步域混淆方法，通过基于频率和注意力规范的掩盖来遮蔽特定于域的线索，并揭示域泛化上下文，从而实现了领域反事实样本的生成，通过实验验证本方法在领域情感分类、对抗领域适应和多领域意图分类任务中的有效性。