Cross-Domain Text Classification

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7 shown of 7 papers with code (14 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Depth F₁: Improving Evaluation of Cross-Domain Text Classification by Measuring Semantic Generalizability
  20 Jun 2024 1 repository listed
  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.
• TACIT: A Target-Agnostic Feature Disentanglement Framework for Cross-Domain Text Classification
  25 Dec 2023 1 repository listed
  However, these methods rely on unlabeled samples provided by the target domains, which renders the model ineffective when the target domain is agnostic.
• Domain Generalization for Text Classification with Memory-Based Supervised Contrastive Learning
  1 Oct 2022 1 repository listed
  While there is much research on cross-domain text classification, most existing approaches focus on one-to-one or many-to-one domain adaptation.
• Multiple-Source Domain Adaptation via Coordinated Domain Encoders and Paired Classifiers
  28 Jan 2022 1 repository listed
  We present a novel multiple-source unsupervised model for text classification under domain shift.
• Cross-Domain Labeled LDA for Cross-Domain Text Classification
  16 Sep 2018 1 repository listed
  To this end, we embed the group alignment and a partial supervision into a cross-domain topic model, and propose a Cross-Domain Labeled LDA (CDL-LDA).
• Hierarchical Attention Transfer Network for Cross-Domain Sentiment Classification
  26 Apr 2018 1 repository listed
  Existing cross-domain sentiment classification meth- ods cannot automatically capture non-pivots, i.
• Multinomial Adversarial Networks for Multi-Domain Text Classification
  15 Feb 2018 1 repository listed
  Many text classification tasks are known to be highly domain-dependent.