Abstract
User-Defined Text Classification (UDTC) considers the challenge of classifying input text to user-specified, previously unseen classes, a setting that arises frequently in real-world applications such as enterprise analytics, content moderation, and domain-specific information retrieval. We propose a soft-contextualized encoder architecture for UDTC which contextualizes each candidate label with the label set and a static soft prompt representation of the input query. Training on diverse, multi-source datasets enables the model to generalize effectively to zero-shot classification over entirely unseen topic sets drawn from arbitrary domains. We evaluate the proposed architecture both on held-out in-distribution test data and on multiple unseen UDTC benchmarks. Across datasets, the model achieves state-of-the-art performance, consistently outperforming or matching the baselines.
Abstract (translated)
用户定义文本分类（UDTC）旨在解决将输入文本分类到用户指定且之前未见过的类别中的挑战，这一问题在企业分析、内容审核和特定领域的信息检索等实际应用中经常出现。我们提出了一种软上下文编码器架构来处理 UDTC 任务，该架构通过结合标签集以及输入查询的静态软提示表示来对每个候选标签进行上下文化处理。基于多样化多源数据集的训练使模型能够有效地实现零样本分类，适用于从任意领域抽取的完全未见过的主题集合。我们不仅在保留的内部测试数据上评估了所提出的架构，还在多个未见过的 UDTC 基准测试中进行了测试。跨不同数据集来看，该模型实现了最先进的性能，在所有情况下都优于或与基线方法相匹配。
URL
https://arxiv.org/abs/2601.03450
