Soft Contextualized Encoder For User Defined Text Classification

• URL: http://arxiv.org/abs/2601.03450v1
• Date: Tue, 06 Jan 2026 22:34:50 GMT
• Title: Soft Contextualized Encoder For User Defined Text Classification
• Abstract summary: User-Defined Text Classification (UDTC) considers the challenge of classifying input text to user-specified, previously unseen classes.<n>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.
• Score: 2.6107088019301252
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

Related papers

• UniPPTBench: A Unified Benchmark for Presentation Generation Across Diverse Input Settings [23.076274859522883]
  Existing works typically focus on presentation generation under isolated input settings.<n>Real-world use cases span diverse scenarios, including vague user prompts, long documents, multimodal materials, and multiple heterogeneous sources.<n>We present UniPPTBench, a unified benchmark for presentation generation across four representative input settings.
  arXiv  Detail & Related papers  (2026-05-17T09:50:16Z)
• Beyond Open Vocabulary: Multimodal Prompting for Object Detection in Remote Sensing Images [52.7196029918473]
  Open-vocabulary object detection in remote sensing commonly relies on text-only prompting to specify target categories.<n>In practice, this assumption often breaks down in remote sensing scenarios due to task- and application-specific category semantics.<n>We propose RS-MPOD, a multimodal open-vocabulary detection framework that reformulates category specification beyond text-only prompting.
  arXiv  Detail & Related papers  (2026-02-02T11:03:01Z)
• SDVPT: Semantic-Driven Visual Prompt Tuning for Open-World Object Counting [70.49268117587562]
  We propose a plug-and-play Semantic-Driven Visual Prompt Tuning framework (SDVPT) that transfers knowledge from the training set to unseen categories.<n>During inference, we dynamically synthesize the visual prompts for unseen categories based on the semantic correlation between unseen and training categories.
  arXiv  Detail & Related papers  (2025-04-24T09:31:08Z)
• Semantic Library Adaptation: LoRA Retrieval and Fusion for Open-Vocabulary Semantic Segmentation [72.28364940168092]
  Open-vocabulary semantic segmentation models associate vision and text to label pixels from an undefined set of classes using textual queries.<n>We introduce Semantic Library Adaptation (SemLA), a novel framework for training-free, test-time domain adaptation.
  arXiv  Detail & Related papers  (2025-03-27T17:59:58Z)
• Empirical Evaluation of Embedding Models in the Context of Text Classification in Document Review in Construction Delay Disputes [6.076874513889027]
  Text embeddings are numerical representations of text data, where words, phrases, or entire documents are converted into vectors of real numbers.<n>This paper presents our work in evaluating different embeddings through a comprehensive comparative analysis of four distinct models.<n>We employ both K-Nearest Neighbors (KNN) and Logistic Regression (LR) to perform binary classification tasks, specifically determining whether a text snippet is associated with 'delay' or 'not delay' within a labeled dataset.
  arXiv  Detail & Related papers  (2025-01-16T22:12:11Z)
• Automated Collection of Evaluation Dataset for Semantic Search in Low-Resource Domain Language [4.5224851085910585]
  Domain-specific languages that use a lot of specific terminology often fall into the category of low-resource languages.<n>This study addresses the challenge of automated collecting test datasets to evaluate semantic search in low-resource domain-specific German language.
  arXiv  Detail & Related papers  (2024-12-13T09:47:26Z)
• Towards Open-Domain Topic Classification [69.21234350688098]
  We introduce an open-domain topic classification system that accepts user-defined taxonomy in real time. Users will be able to classify a text snippet with respect to any candidate labels they want, and get instant response from our web interface.
  arXiv  Detail & Related papers  (2023-06-29T20:25:28Z)
• TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision [61.186488081379]
  We propose TextFormer, a query-based end-to-end text spotter with Transformer architecture. TextFormer builds upon an image encoder and a text decoder to learn a joint semantic understanding for multi-task modeling. It allows for mutual training and optimization of classification, segmentation, and recognition branches, resulting in deeper feature sharing.
  arXiv  Detail & Related papers  (2023-06-06T03:37:41Z)
• CLIP-GCD: Simple Language Guided Generalized Category Discovery [21.778676607030253]
  Generalized Category Discovery (GCD) requires a model to both classify known categories and cluster unknown categories in unlabeled data. Prior methods leveraged self-supervised pre-training combined with supervised fine-tuning on the labeled data, followed by simple clustering methods. We propose to leverage multi-modal (vision and language) models, in two complementary ways.
  arXiv  Detail & Related papers  (2023-05-17T17:55:33Z)
• Generating More Pertinent Captions by Leveraging Semantics and Style on Multi-Source Datasets [56.018551958004814]
  This paper addresses the task of generating fluent descriptions by training on a non-uniform combination of data sources. Large-scale datasets with noisy image-text pairs provide a sub-optimal source of supervision. We propose to leverage and separate semantics and descriptive style through the incorporation of a style token and keywords extracted through a retrieval component.
  arXiv  Detail & Related papers  (2021-11-24T19:00:05Z)
• ConCET: Entity-Aware Topic Classification for Open-Domain Conversational Agents [9.870634472479571]
  We introduce ConCET: a Concurrent Entity-aware conversational Topic classifier. We propose a simple and effective method for generating synthetic training data. We evaluate ConCET on a large dataset of human-machine conversations with real users, collected as part of the Amazon Alexa Prize.
  arXiv  Detail & Related papers  (2020-05-28T06:29:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.