软上下文编码器用于用户定义的文本分类
Soft Contextualized Encoder For User Defined Text Classification
AI总结：
本文提出了一种软上下文编码器架构，用于解决用户定义的文本分类问题，通过上下文化标签和静态提示表示，实现对未知类别的有效分类。
摘要：
用户定义的文本分类（UDTC）考虑了将输入文本分类到用户指定的、此前未见过的类别中的挑战，这种设置在现实世界应用中经常出现，如企业分析、内容审核和领域特定信息检索。我们提出了一种软上下文编码器架构用于UDTC，该架构通过标签集和输入查询的静态软提示表示来上下文化每个候选标签。在多样、多源数据集上训练使模型能够有效地泛化到完全未见过的主题集上，这些主题集来自任意领域。我们评估了所提出的架构，既在保留分布的测试数据上，也多个未见过的UDTC基准上。在各数据集上，该模型实现了最先进的性能，一致地优于或匹配基线。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
