Papers with Self-training
Neural Self-Training through Spaced Repetition (N19-1)
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| Challenge: | Existing methods for self-training rely on predetermined policies to sample unlabeled data. |
| Approach: | They propose a semi-supervised learning approach that uses spaced repetition to dynamically sample informative and diverse unlabeled instances with respect to individual learner and instance characteristics. |
| Outcome: | The proposed model outperforms existing semi-supervised learning approaches on publicly-available datasets. |
Handling Noisy Labels for Robustly Learning from Self-Training Data for Low-Resource Sequence Labeling (N19-3)
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| Challenge: | In low-resource environments, self-training is less effective due to unreliable annotations . we combine self-teaching with noise handling to clean the self-labeled data . |
| Approach: | They propose to combine self-training with noise handling to clean unlabeled data . they propose to model clean and noisy labels separately to improve performance . |
| Outcome: | The proposed method performs better than baseline methods on Chunking and NER. |
SPPD: Self-training with Process Preference Learning Using Dynamic Value Margin (2025.findings-emnlp)
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| Challenge: | Existing approaches to improve numerical and logical reasoning of Large Language Models are limited . existing approaches rely on prompt engineering and pretrained knowledge to ensure correctness . |
| Approach: | They propose to train LLMs with process-based reasoning using a dynamic value margin . they use the Bellman optimality equation to derive a value margin for step-level preference optimization . |
| Outcome: | The proposed method is equivalent to on-policy policy gradient methods under constrained reward functions. |
DuRE: Dual Contrastive Self Training for Semi-Supervised Relation Extraction (2024.naacl-long)
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| Challenge: | Existing document-level relation extraction methods require manual training and labeled data to obtain supervised learning. |
| Approach: | They propose a document-level relation extraction framework that integrates RE and text generation as a dual process. |
| Outcome: | The proposed framework significantly boosts recall and F1 score with comparable precision on two document-level RE tasks against several strong baselines. |
CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking (2022.aacl-short)
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| Challenge: | Existing few-shot dialogue state tracking (DST) methods transfer knowledge from labeled data into DST, but collecting large amount of labeles is laborious. |
| Approach: | They propose a few-shot dialogue state tracking framework that integrates self-training and self-supervised learning methods into the framework. |
| Outcome: | The proposed framework achieves competitive performance in several few-shot scenarios. |
Self-Training with Differentiable Teacher (2022.findings-naacl)
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| Challenge: | Existing methods for self-training are interpreted as teacher-student frameworks, where the teacher generates pseudo-labels and the student makes predictions. |
| Approach: | They propose a differentiable self-training method that treats teacher-student as a Stackelberg game where a leader is always in a more advantageous position than a follower. |
| Outcome: | The proposed model outperforms existing methods on semi- and weakly-supervised learning tasks on semi and weak supervised tasks. |
Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding (2021.findings-emnlp)
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| Challenge: | Task-adaptive pre-training (TAPT) and Self-training can be complementary with simple TFS protocol. |
| Approach: | They propose to use task-adaptive pre-training and self-training to combine TAPT and ST with a simple TFS protocol to achieve strong combined gains across six datasets. |
| Outcome: | The proposed method can achieve strong combined gains across six datasets covering sentiment classification, paraphrase identification, natural language inference, named entity recognition and dialogue slot classification. |
Self-training Strategies for Sentiment Analysis: An Empirical Study (2024.findings-eacl)
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| Challenge: | Sentiment analysis is a crucial task in natural language processing. |
| Approach: | They propose to leverage a small amount of labeled and unlabeled data to train models with self-training. |
| Outcome: | The proposed method improves the performance of small language models in several few-shot settings while reducing the cost of annotations. |
Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation (2021.acl-long)
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| Challenge: | Experimental results show that enhancing the learning on uncertain monolingual sentences improves the translation quality of high-uncertainty sentences and also benefits the prediction of low-frequency words at the target side. |
| Approach: | They propose to use monolingual data to augment model training with synthetic parallel data by selecting the most informative monolingual sentences to complement the parallel data. |
| Outcome: | The proposed approach improves the performance of natural language models by selecting the most informative monolingual sentences. |
A Fine-Grained Domain Adaption Model for Joint Word Segmentation and POS Tagging (2021.emnlp-main)
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| Challenge: | Experimental results show that joint models of word segmentation and POS tagging can lead to better performance because they are closely related. |
| Approach: | They propose a domain adaption method for Chinese word segmentation and POS tagging that uses a simple metric to model the gaps between target and target domains. |
| Outcome: | The proposed method can gain significant performance improvements over baselines on a benchmark dataset. |
Rethinking Semi-supervised Learning with Language Models (2023.findings-acl)
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| Challenge: | Semi-supervised learning (SSL) is a popular setting to make use of unlabelled data . Currently, there are two popular approaches to make effective use of the unlabelled datasets . |
| Approach: | They compare semi-supervised learning (SSL) and task-adaptive pre-training (TAPT) they find TAPT is a stronger and more robust SSL learner, even when using just a few hundred unlabelled samples . |
| Outcome: | The proposed methods improve model performance across different NLP tasks and data sizes. |
Joint Speech Transcription and Translation: Pseudo-Labeling with Out-of-Distribution Data (2023.findings-acl)
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| Challenge: | a recent study shows that self-training can improve upon fully supervised baselines in low-resource settings for several sequence-to-sequence tasks. |
| Approach: | They propose to use pseudo-labeling to label unsupervised data and add it to the training pool. |
| Outcome: | The proposed setup improves on the unsupervised data by using pseudo-labeling . the proposed setup provides 0.4% absolute WER and 2.1 BLEU points for En–De . |
DuNST: Dual Noisy Self Training for Semi-Supervised Controllable Text Generation (2023.acl-long)
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| Challenge: | Existing approaches to augment self-training (ST) in attribute-controllable language generation are limited and limited. |
| Approach: | They propose a new ST framework that integrates self-generated pseudo text into attribute-controllable language generation. |
| Outcome: | The proposed framework can be applied to semi-supervised controllable language generation. |
GALA: Geometric Data Selection with Strategic Prospecting for Large Language Model Self-training (2026.findings-acl)
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| Challenge: | Existing approaches to self-training are based on reject sampling and lack quality reasoning paths. |
| Approach: | They propose a framework for self-training using a generate-and-filter paradigm . they propose to identify diverse and informative samples from redundant data and exploit them more strategically. |
| Outcome: | The proposed framework exploits informative samples from redundant data and improves reasoning trajectory prospecting. |
LLM-enhanced Self-training for Cross-domain Constituency Parsing (2023.emnlp-main)
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| Challenge: | Existing approaches to self-training rely on limited and potentially low-quality raw corpora. |
| Approach: | They propose to enhance self-training with the large language model to generate domain-specific raw corpora iteratively and introduce grammar rules that guide the LLM in generating raw corporeals and establish criteria for selecting pseudo instances. |
| Outcome: | The proposed method outperforms traditional methods regardless of the large language model's performance. |
Class-Adaptive Self-Training for Relation Extraction with Incompletely Annotated Training Data (2023.findings-acl)
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| Challenge: | Existing relation extraction models rely on supervised machine learning, but many datasets are incompletely annotated, causing false negatives and errors during inference stage. |
| Approach: | They propose a class-adaptive re-sampling self-training framework that favored the pseudo-labels of classes with high precision and low recall scores. |
| Outcome: | The proposed framework outperforms existing methods on the Re-DocRED and ChemDisgene datasets when the training data are incompletely annotated. |
Feature Adaptation of Pre-Trained Language Models across Languages and Domains with Robust Self-Training (2020.emnlp-main)
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| Challenge: | Adapting pre-trained language models (PrLMs) to new domains has gained much attention . Adaptation of PrLMs to newdomains is important, but requires fine-tuning . |
| Approach: | They propose to use PrLMs to adapt to new domains without fine-tuning . they use class-aware feature self-distillation to learn discriminative features . |
| Outcome: | The proposed model can learn discriminative features from pre-trained language models without fine-tuning. |
Re-ReST: Reflection-Reinforced Self-Training for Language Agents (2024.emnlp-main)
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| Challenge: | Existing methods to fine tune language agents with reasoning-action trajectories require high-quality model-generated samples, which are hard to obtain for challenging language agent tasks. |
| Approach: | They propose a method to employ reflection during inference without ground-truth feedback to improve agents more autonomously. |
| Outcome: | The proposed method improves self-training performance on open-source language agents by 7.6% and 14.1% respectively. |