Papers with text classification benchmarks
SaFER: A Robust and Efficient Framework for Fine-tuning BERT-based Classifier with Noisy Labels (2023.acl-industry)
Copied to clipboard
| Challenge: | Existing noise-handling methods could not improve performance of BERT on noisy datasets . existing methods could only improve performance on noisy data, authors say . |
| Approach: | They propose a fine-tuning framework for BERT-based text classifiers that combats label noises without access to clean data for training or validation. |
| Outcome: | The proposed framework achieves superior performance on multiple text classification benchmarks. |
OoMMix: Out-of-manifold Regularization in Contextual Embedding Space for Text Classification (2021.acl-long)
Copied to clipboard
| Challenge: | Recent studies on neural networks with pre-trained weights focus on low-dimensional subspace where the embedding vectors computed from input words are located. |
| Approach: | They propose an approach to find and regularize the remainder of the space, referred to as out-of-manifold, which cannot be accessed through the words. |
| Outcome: | The proposed approach is able to fine-tune the out-of-manifold embedding space on text classification benchmarks. |
LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-Tuning (2023.acl-short)
Copied to clipboard
| Challenge: | Recent advances in pre-trained language models have been limited when fine-tuned on small datasets. |
| Approach: | They propose to add contrastive learning to prompt-based fine-tuning to improve model performance. |
| Outcome: | The proposed approach outperforms other methods on multiple text classification benchmarks. |
SelfMix: Robust Learning against Textual Label Noise with Self-Mixup Training (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods to handle label noise in text classification tasks are limited to visual data. |
| Approach: | They propose a method to handle label noise in text classification tasks using a Gaussian Mixture Model. |
| Outcome: | The proposed method outperforms baselines on three types of text classification tasks on visual and textual data. |
Pseudo Outlier Exposure for Out-of-Distribution Detection using Pretrained Transformers (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to detect out-of-distribution (OOD) samples are overconfident for real-world language applications. |
| Approach: | They propose a method that constructs a surrogate OOD dataset by sequentially masking tokens related to ID classes. |
| Outcome: | The proposed method can train a rejection network with ID and diverse outlier samples but requires additional data collection overhead. |
Revisiting Uncertainty-based Query Strategies for Active Learning with Transformers (2022.findings-acl)
Copied to clipboard
| Challenge: | a recent study has investigated how transformer-based language models can be combined with active learning. |
| Approach: | They propose to combine transformer-based language models with active learning to reduce labeling costs . transformers are expensive, but they can be fine-tuned using a query strategy . they compare transformers to experiments from previous research to evaluate their performance . |
| Outcome: | The proposed model outperforms the well-known prediction entropy query strategy on five widely used text classification benchmarks. |
MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification (2020.acl-main)
Copied to clipboard
| Challenge: | Xie et al., 2016) demonstrate that semi-supervised learning models suffer from over-fitting when there is only limited labeled data. |
| Approach: | They propose a semi-supervised learning method for text classification using a data augmentation method called TMix. |
| Outcome: | The proposed method outperforms pre-trained and fine-tuned models on several text classification benchmarks. |
ProtoLens: Advancing Prototype Learning for Fine-Grained Interpretability in Text Classification (2025.acl-long)
Copied to clipboard
| Challenge: | ProtoLens provides fine-grained, sub-sentence level interpretability for text classification. |
| Approach: | They propose a prototype-based model that provides fine-grained, sub-sentence level interpretability for text classification. |
| Outcome: | Extensive experiments show that ProtoLens outperforms both prototype-based and non-interpretable baselines on multiple text classification benchmarks. |
Adversarial Mixing Policy for Relaxing Locally Linear Constraints in Mixup (2021.emnlp-main)
Copied to clipboard
| Challenge: | Experiments on five text classification benchmarks and five backbone models have shown that our methods reduce the error rate over Mixup variants in a significant margin (up to 31.3%), especially in low-resource conditions (upto 17.5%). |
| Approach: | They propose to add a small adversarial perturbation to the mixing coefficients rather than the examples to relax locally linear constraints. |
| Outcome: | Experiments on five text classification benchmarks and five backbone models show that the proposed methods reduce the error rate over Mixup variants by 31.3%, especially in low-resource conditions. |
SSMix: Saliency-Based Span Mixup for Text Classification (2021.findings-acl)
Copied to clipboard
| Challenge: | SSMix synthesizes a sentence while preserving the locality of two original texts by span-based mixing and keeping more tokens related to the prediction relying on saliency information. |
| Approach: | They propose a new method where the operation is performed on input text rather than on hidden vectors like previous approaches. |
| Outcome: | The proposed method outperforms hidden-level mixup methods on a wide range of text classification benchmarks including textual entailment, sentiment classification, and questiontype classification. |
Investigating Capsule Networks with Dynamic Routing for Text Classification (D18-1)
Copied to clipboard
| Challenge: | Earlier efforts in text modeling have achieved limited success on word meanings . convolutional neural networks (CNNs) are used to model higher level concepts and facts in texts . |
| Approach: | They propose three strategies to stabilize dynamic routing process to alleviate disturbance of noise capsules. |
| Outcome: | The proposed methods achieve state-of-the-art on 4 out of 6 datasets . they show that capsule networks exhibit significant improvement over baseline methods . |
TeCoFeS: Text Column Featurization using Semantic Analysis (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for topic modeling and feature extraction are based on syntactic features and overlook the semantics. |
| Approach: | They propose a semantic text column featurization problem that extracts a small sample smartly using an LLM to label only the sample and then extends that labeling to the whole column using text embeddings. |
| Outcome: | The proposed approach performs better than baselines and naive use of LLMs. |
Self-training Improves Pre-training for Natural Language Understanding (2021.naacl-main)
Copied to clipboard
Jingfei Du, Edouard Grave, Beliz Gunel, Vishrav Chaudhary, Onur Celebi, Michael Auli, Veselin Stoyanov, Alexis Conneau
| Challenge: | Unsupervised pretraining has led to improvements in natural language understanding . a data augmentation method can be used to generate labels for unlabeled examples . |
| Approach: | They propose a semi-supervised method which uses unlabeled data to retrieve sentences from a database of billions of unlabed sentences crawled from the web. |
| Outcome: | The proposed method improves on standard text classification benchmarks by 2.6% and knowledge distillation by few shots. |
Self-Training for Sample-Efficient Active Learning for Text Classification with Pre-Trained Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to train models without labeled data are lacking in supervised tasks . a lack of labeles is the main obstacle to real-world applications . |
| Approach: | They propose a semi-supervised approach that uses a model to obtain pseudo-labels for unlabeled data. |
| Outcome: | The proposed method outperforms the reproduced methods on four text classification benchmarks. |
Efficient Unseen Language Adaptation for Multilingual Pre-Trained Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Multilingual pre-trained language models (mPLMs) have demonstrated notable effectiveness in zero-shot cross-lingual transfer tasks. |
| Approach: | They propose a method that uses soft-prompt tuning to tune for language adaptation . prompt tuning outperforms continuously trained baselines on two benchmarks . |
| Outcome: | The proposed approach outperforms baselines on two text classification benchmarks while utilizing 0.28% of tuned parameters. |
Beyond Task-Level Context: Class-Conditional Context Vectors for Implicit In-Context Learning (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches aggregate demonstrations from all classes into a shared, task-level context vector, capturing global task information but without explicitly preserving fine-grained, class-conditional semantic distinctions. |
| Approach: | They propose a class-conditional context vector extension to implicit in-context learning that explicitly models class-specific contextual information by constructing separate context vectors for each class. |
| Outcome: | The proposed extension outperforms task-level context vector baselines and achieves higher average accuracy than conventional few-shot learning on most models. |