Multi-View Active Learning for Short Text Classification in User-Generated Data (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Using a microblog, a set of tasks can be tailored for queries. |
| Approach: | They propose a novel active learning model to overcome the lack of labeled data and the informal user language in mining user-generated data. |
| Outcome: | The proposed model outperforms existing models in four publicly available Twitter datasets and in four datasets with different applications. |
Similar Papers
Investigating Active Learning Sampling Strategies for Extreme Multi Label Text Classification (2022.lrec-1)
Copied to clipboard
| Challenge: | Large scale, multi-label text datasets with high numbers of different classes are expensive to annotate due to domain experts taking a lot of time working through all the classes. |
| Approach: | They propose to build classifiers on multi-label text datasets using Active Learning to reduce labeling effort. |
| Outcome: | The proposed classifiers can be used to reduce labeling effort on multi-label datasets. |
Small-Text: Active Learning for Text Classification in Python (2023.eacl-demo)
Copied to clipboard
| Challenge: | small-text is an easy-to-use active learning library for text classification . it features a variety of pre-implemented state-of-the-art query strategies and stopping criteria . |
| Approach: | They introduce small-text, an easy-to-use active learning library for Python . it offers pool-based active learning for single- and multi-label text classification . they find it matches vanilla transformer fine-tuning in terms of classification accuracy . |
| Outcome: | The proposed library outperforms vanilla transformer fine-tuning in classification accuracy and area under the curve. |
Multi-View Document Representation Learning for Open-Domain Dense Retrieval (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods for dense retrieval are hard to match with multiple views. |
| Approach: | They propose a multi-view document representation learning framework to generate multiple embeddings through viewers to represent documents and enforce them to align with different queries. |
| Outcome: | The proposed method outperforms recent works and achieves state-of-the-art results. |
Reinforced Active Learning for Low-Resource, Domain-Specific, Multi-Label Text Classification (2023.findings-acl)
Copied to clipboard
| Challenge: | Modern text classification systems achieve excellent accuracy across tasks and corpora. |
| Approach: | They propose a Reinforcement Learning policy that uses many different aspects of the data and task to select the most informative unlabeled subset dynamically over the course of the AL procedure. |
| Outcome: | The proposed framework outperforms baselines on four complex multi-class, multi-label text classification datasets. |
Active Few-Shot Learning for Text Classification (2025.naacl-long)
Copied to clipboard
Saeed Ahmadnia, Arash Yousefi Jordehi, Mahsa Hosseini Khasheh Heyran, Seyed Abolghasem Mirroshandel, Owen Rambow, Cornelia Caragea
| Challenge: | Recent advances in Large Language Models (LLMs) have boosted the use of Few-Shot Learning (FSL) methods in natural language processing. |
| Approach: | They propose a method that identifies effective support instances from the unlabeled pool and can work with different LLMs. |
| Outcome: | The proposed method improves on five tasks on which it is tested on five LLMs. |
Investigating Multi-source Active Learning for Natural Language Inference (2023.eacl-main)
Copied to clipboard
| Challenge: | Recent studies often assume that training and test data are drawn from the same distribution. |
| Approach: | They propose to apply active learning to unlabelled data pools to test for learning and generalisation. |
| Outcome: | The proposed strategies outperform random selection and outperformed hard-to-learn data on the task of natural language inference. |
A Survey of Active Learning for Natural Language Processing (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing literature surveys on active learning for NLP are too specific or too general, covering deep active learning. |
| Approach: | They propose to use active learning to improve model learning and annotation cost for NLP problems. |
| Outcome: | The proposed approach is based on a large dataset of data-driven machine learning models. |
ActiveLLM: Large Language Model-Based Active Learning for Textual Few-Shot Scenarios (2026.tacl-1)
Copied to clipboard
| Challenge: | Active learning strategies struggle with a ‘cold-start’ problem, needing substantial initial data to be effective. |
| Approach: | They propose an active learning approach that leverages Large Language Models such as GPT-4, o1, Llama 3, or Mistral Large for selecting instances. |
| Outcome: | The proposed approach outperforms existing methods ADAPET, PERFECT, and SetFit in few-shot scenarios and can be extended to non-few scenarios. |
Exploring the Value of Multi-View Learning for Session-Aware Query Representation (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing approaches to learn distributed query representations only consider user’s query reformulations or system’s rankings . previous studies show that user’ s query behavior and knowledge change depending on the system’ 'results' and intertwine and affect each other during the completion of a search task. |
| Approach: | They propose to use multi-view learning methods to align query embeddings with document ranking representations using transformers. |
| Outcome: | The proposed approach can capture search intent semantics and can reflect user's query behavior and knowledge. |
FAID: Fine-grained AI-generated Text Detection using Multi-task Auxiliary and Multi-level Contrastive Learning (2026.eacl-long)
Copied to clipboard
Minh Ngoc Ta, Dong Cao Van, Duc-Anh Hoang, Minh Le-Anh, Truong Nguyen, My Anh Tran Nguyen, Yuxia Wang, Preslav Nakov, Dinh Viet Sang
| Challenge: | Existing binary detection frameworks for human-written, LLM-generated and human-LLM collaborative texts are challenging . a recent study focused on binary detection, i.e., human vs. LLM, or on fine-grained detection limited to English. |
| Approach: | They propose a fine-grained detection framework to classify text into three categories . they use multilingual datasets and a multi-domain, multi-generator dataset . |
| Outcome: | The proposed framework outperforms baselines on unseen domains and new LLMs. |