Papers Cross-Domain Text Classification

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

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…

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

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 …

A Two-Stage Framework with Self-Supervised Distillation 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 targe…

A New cross-domain strategy based XAI models for fake news detection

In this study, we presented a four-level cross-domain strategy for fake news detection on pre-trained models. Cross-domain text classification is a task of a model adopting a target domain by using the knowledge of the s…

Domain Generalization for Text Classification with Memory-Based Supervised Contrastive Learning

While there is much research on cross-domain text classification, most existing approaches focus on one-to-one or many-to-one domain adaptation. In this paper, we tackle the more challenging task of domain generalization…

Multiple-Source Domain Adaptation via Coordinated Domain Encoders and Paired Classifiers

We present a novel multiple-source unsupervised model for text classification under domain shift. Our model exploits the update rates in document representations to dynamically integrate domain encoders. It also employs …

Matching Distributions between Model and Data: Cross-domain Knowledge Distillation for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) aims to transfer the knowledge of source domain to the unlabeled target domain. Existing methods typically require to learn to adapt the target model by exploiting the source data and…

Unsupervised Energy-based Adversarial Domain Adaptation for Cross-domain Text Classification

Transductive Learning with String Kernels for Cross-Domain Text Classification

For many text classification tasks, there is a major problem posed by the lack of labeled data in a target domain. Although classifiers for a target domain can be trained on labeled text data from a related source domain…

Cross-Domain Labeled LDA for Cross-Domain Text Classification

Cross-domain text classification aims at building a classifier for a target domain which leverages data from both source and target domain. One promising idea is to minimize the feature distribution differences of the tw…

Hierarchical Attention Transfer Network for Cross-Domain Sentiment Classification

Cross-domain sentiment classification aims to leverage useful information in a source domain to help do sentiment classifi- cation in a target domain that has no or little supervised infor- mation. Existing cross-domain …

Multinomial Adversarial Networks for Multi-Domain Text Classification

Many text classification tasks are known to be highly domain-dependent. Unfortunately, the availability of training data can vary drastically across domains. Worse still, for some domains there may not be any annotated d…

Cross-domain Text Classification with Multiple Domains and Disparate Label Sets

An Iterative Similarity based Adaptation Technique for Cross-domain Text Classification