Recent evaluations of cross-domain text classification models aim to measure
the ability of a model to obtain domain-invariant performance in a target
domain given labeled samples in a source domain. The primary strategy for this
evaluation relies on assumed differences between source domain samples and
target domain samples in benchmark datasets. This evaluation strategy fails to
account for the similarity between source and target domains, and may mask when
models fail to transfer learning to specific target samples which are highly
dissimilar from the source domain. We introduce Depth $F_1$, a novel
cross-domain text classification performance metric. Designed to be
complementary to existing classification metrics such as $F_1$, Depth $F_1$
measures how well a model performs on target samples which are dissimilar from
the source domain. We motivate this metric using standard cross-domain text
classification datasets and benchmark several recent cross-domain text
classification models, with the goal of enabling in-depth evaluation of the
semantic generalizability of cross-domain text classification models.

该研究介绍了一种新颖的跨领域文本分类性能度量标准 Depth F1，用于评估模型在源域和目标域之间的语义泛化能力，该标准衡量了模型在与源域高度不相似的目标样本上的表现。通过在几个最新的跨领域文本分类模型上进行基准测试，旨在促进对跨领域文本分类模型的语义泛化能力进行深入评估。

深度 $F_1$：通过度量语义泛化性提高跨领域文本分类评估

Depth $F_1$: Improving Evaluation of Cross-Domain Text Classification by  Measuring Semantic Generalizability

Cross-domain text classification aims to transfer models from label-rich
source domains to label-poor target domains, giving it a wide range of
practical applications. Many approaches promote cross-domain generalization by
capturing domain-invariant features. However, these methods rely on unlabeled
samples provided by the target domains, which renders the model ineffective
when the target domain is agnostic. Furthermore, the models are easily
disturbed by shortcut learning in the source domain, which also hinders the
improvement of domain generalization ability. To solve the aforementioned
issues, this paper proposes TACIT, a target domain agnostic feature
disentanglement framework which adaptively decouples robust and unrobust
features by Variational Auto-Encoders. Additionally, to encourage the
separation of unrobust features from robust features, we design a feature
distillation task that compels unrobust features to approximate the output of
the teacher. The teacher model is trained with a few easy samples that are easy
to carry potential unknown shortcuts. Experimental results verify that our
framework achieves comparable results to state-of-the-art baselines while
utilizing only source domain data.

提出了一种名为 TACIT 的目标域不可知特征分解框架，通过变分自动编码器自适应解耦强韧特征和非强韧特征，并设计了一项特征蒸馏任务来促使非强韧特征逼近教师模型输出，实验证明仅利用源领域数据时，我们的框架实现了与最先进基线方法相当的结果。

TACIT: 一个目标不可知特征解缠框架用于跨领域文本分类

TACIT: A Target-Agnostic Feature Disentanglement Framework for  Cross-Domain Text Classification

Cross-domain text classification aims to adapt models to a target domain that
lacks labeled data. It leverages or reuses rich labeled data from the different
but related source domain(s) and unlabeled data from the target domain. To this
end, previous work focuses on either extracting domain-invariant features or
task-agnostic features, ignoring domain-aware features that may be present in
the target domain and could be useful for the downstream task. In this paper,
we propose a two-stage framework for cross-domain text classification. In the
first stage, we finetune the model with mask language modeling (MLM) and
labeled data from the source domain. In the second stage, we further fine-tune
the model with self-supervised distillation (SSD) and unlabeled data from the
target domain. We evaluate its performance on a public cross-domain text
classification benchmark and the experiment results show that our method
achieves new state-of-the-art results for both single-source domain adaptations
(94.17% $\uparrow$1.03%) and multi-source domain adaptations (95.09%
$\uparrow$1.34%).

本文提出了一种二阶段跨域文本分类的框架，使用掩蔽语言模型和标记数据从源域进行模型微调，然后使用自监督蒸馏和目标域的无标注数据进一步微调模型，实验结果表明我们的方法在单源域（94.17％ ↑1.03％）和多源域（95.09％ ↑1.34％）适应都达到了最新的最先进水平。