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arXiv · 2601.03450

Soft Contextualized Encoder For User Defined Text Classification

Charu Maheshwari·Vyas Raina
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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.

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Charu Maheshwari, Vyas Raina. 2026-01-06. Soft Contextualized Encoder For User Defined Text Classification. https://arxiv.org/abs/2601.03450

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