mlsanityData to 7 Oct 2026

Multi-class Text Classification using BERT-based Active Learning

arXiv 2104.14289 ↗First posted 27 Apr 2021cs.IR cs.LGcited by 11

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Abstract (arXiv)

Text Classification finds interesting applications in the pickup and delivery services industry where customers require one or more items to be picked up from a location and delivered to a certain destination. Classifying these customer transactions into multiple categories helps understand the market needs for different customer segments. Each transaction is accompanied by a text description provided by the customer to describe the products being picked up and delivered which can be used to classify the transaction. BERT-based models have proven to perform well in Natural Language Understanding. However, the product descriptions provided by the customers tend to be short, incoherent and code-mixed (Hindi-English) text which demands fine-tuning of such models with manually labelled data to achieve high accuracy. Collecting this labelled data can prove to be expensive. In this paper, we explore Active Learning strategies to label transaction descriptions cost effectively while using BERT to train a transaction classification model. On TREC-6, AG's News Corpus and an internal dataset, we benchmark the performance of BERT across different Active Learning strategies in Multi-Class Text Classification.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 20 of its 49 references that are in the map. First posted on arXiv 27 Apr 2021.

Builds on

20 of 49 references.

References · 20

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
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8 more
  1. 2015Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inferencecited by 159
  2. 2019Discriminative Active Learningcited by 97
  3. 2018Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Studycited by 90
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 4; 2024: 0; 2025: 4; 2026: 1.

11 papers.

  1. 2023Understanding Telecom Language Through Large Language Modelscited by 32
  2. 2023Stance Detection: A Practical Guide to Classifying Political Beliefs in Textcited by 20
  3. 2023ActiveGLAE: A Benchmark for Deep Active Learning with Transformerscited by 14
  4. 2022Multilingual Detection of Personal Employment Status on Twittercited by 8
  5. 2025Reasoner for Real-World Event Detection: Scaling Reinforcement Learning via Adaptive Perplexity-Aware Sampling Strategycited by 3
  6. 2025Improving the Efficiency of Long Document Classification using Sentence Ranking Approachcited by 1
  7. 2025Advancing Text Classification with Large Language Models and Neural Attention Mechanismscited by 1
  8. 2022Nearest Neighbor Classifier with Margin Penalty for Active Learningcited by 0
  9. 2023Utilizing Weak Supervision To Generate Indonesian Conservation Datasetcited by 0
  10. 2025Efficient Zero-Shot Long Document Classification by Reducing Context Through Sentence Rankingcited by 0
  11. 2026Automating the Detection of Requirement Dependencies Using Large Language Modelscited by 0