Papers with self-training
ExtraPhrase: Efficient Data Augmentation for Abstractive Summarization (2022.naacl-srw)
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| Challenge: | Recent studies indicated that neural methods are governed by the scaling law for the amount of training data. |
| Approach: | They propose a low-cost strategy to augment training data for abstractive summarization tasks by extracting summarized text and paraphrasing it. |
| Outcome: | The proposed strategy outperforms back-translation and self-training and is more cost-efficient when training data is small. |
Bootstrapping Neural Relation and Explanation Classifiers (2023.acl-short)
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| Challenge: | supervised approaches that use only rules to explain the outputs of the relation classifier are data hungry and expensive to obtain. |
| Approach: | They propose a method that self trains (or bootstraps) neural relation and explanation classifiers by iterating the outputs into rules and applying them to unlabeled text to produce new annotations. |
| Outcome: | The proposed method outperforms the rule-based model on the TACRED dataset by 15 F1 points and performs comparatively with the prompt-based approach without an additional natural language inference component. |
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. |
Keep Learning: Self-supervised Meta-learning for Learning from Inference (2021.eacl-main)
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| Challenge: | A common approach to improve performance of machine learning algorithms involves self-supervised learning on large unlabeled data before fine-tuning on downstream tasks. |
| Approach: | They propose to use model's own class-balanced predictions to back-propagate the loss from the model''s class-balancing predictions (pseudo-labels) this method improves performance of standard backbones such as BERT, Electra, and ResNet-50 on a wide variety of tasks, including question answering on SQuAD and NewsQA . |
| Outcome: | The proposed method outperforms previous approaches on a wide variety of tasks including question answering on SQuAD and NewsQA, benchmark task SuperGLUE, conversation response selection on Ubuntu Dialog corpus v2.0, and image classification on MNIST and ImageNet. |
Out-of-Domain Discourse Dependency Parsing via Bootstrapping: An Empirical Analysis on Its Effectiveness and Limitation (2022.tacl-1)
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| Challenge: | Discourse parsing accuracy degrades significantly on out-of-domain text. |
| Approach: | They propose to use bootstrapping methods to adapt modern discourse dependency parsers to out-of-domain text without additional human supervision. |
| Outcome: | The proposed methods are significantly and consistently effective for unsupervised domain adaptation of discourse dependency parsing, but the low coverage of accurately predicted pseudo labels is a bottleneck for further improvement. |
Facebook AI’s WAT19 Myanmar-English Translation Task Submission (D19-52)
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Peng-Jen Chen, Jiajun Shen, Matthew Le, Vishrav Chaudhary, Ahmed El-Kishky, Guillaume Wenzek, Myle Ott, Marc’Aurelio Ranzato
| Challenge: | Using back-translation, we can improve generalization by using noisy channel re-ranking and ensembling. |
| Approach: | They propose to use BPE-based transformer models to leverage monolingual data to improve generalization and use noisy channel re-ranking and ensembling to improve results. |
| Outcome: | The proposed system improves on the baseline system trained exclusively on the provided small parallel dataset, and the human evaluation and BLEU score are higher. |
Self-Vocabularizing Training for Neural Machine Translation (2025.naacl-srw)
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| Challenge: | Past vocabulary learning techniques identify relevant vocabulary before training, relying on corpus statistics or frequency counts without considering contextual information or the model's ability to represent it. |
| Approach: | They propose a method that self-vocabularizes a smaller, more optimal vocabulary by pairing source sentences with the model's predictions to define a new vocabulary. |
| Outcome: | The proposed method produces a 1.49 BLEU improvement in the simulated model and an increase in unique token usage and a 6–8% reduction in vocabulary size. |
Friend-training: Learning from Models of Different but Related Tasks (2023.eacl-main)
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| Challenge: | Current self-training methods focus on improving model performance on a single task. |
| Approach: | They propose a cross-task self-training framework where models trained to do different tasks are used in iterative training, pseudo-labeling, and retraining processes to help each other for better selection of pseudo-labeled labels. |
| Outcome: | The proposed framework achieves the best performance compared to baselines on two dialogue understanding tasks. |
Progressive Self-Training with Discriminator for Aspect Term Extraction (2021.emnlp-main)
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| Challenge: | Existing approaches to extract aspect terms from review sentences are limited due to lack of annotated data. |
| Approach: | They propose to refine conventional self-training to progressive self-teaching to reduce noise . they use a discriminator to filter the noisy pseudo-labels. |
| Outcome: | The proposed model outperforms baseline models and achieves state-of-the-art performance on four SemEval datasets. |
LEAF: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Models (2025.emnlp-industry)
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| Challenge: | Large language models (LLMs) struggle with factual accuracy in knowledge-intensive domains like healthcare. |
| Approach: | They propose a framework for improving LLM factuality in medical question answering . RAFE, Fact-Check-then-RAG and Learning from Fact Check are components . |
| Outcome: | Experimental results show that LEAF outperforms Factcheck-GPT in detecting inaccuracies and corrects errors without labeling . the framework provides a scalable solution for industrial applications requiring high factuality scores. |
Transductive Auxiliary Task Self-Training for Neural Multi-Task Models (D19-61)
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| Challenge: | Multi-task learning and self-training are two common ways to improve a machine learning model’s performance in settings with limited training data. |
| Approach: | They propose a transductive auxiliary task self-training procedure that trains a model on auxiliary tasks and test instances with auxiliary labels generated by a single-task version of the model. |
| Outcome: | The proposed method improves accuracy by 9.56% over the pure multi-task model for dependency relation tagging and 13.03% for semantic taging. |
Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study (2022.findings-emnlp)
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| Challenge: | Existing approaches to extract relational facts from text are limited in their ability to learn from limited labeled data. |
| Approach: | They propose to use prompt-based methods with few-shot labeled data to evaluate performance . data augmentation technologies and self-training are also proposed to generate more labeles in-domain data. |
| Outcome: | The proposed methods perform well in low-resource settings with 8 relation extraction datasets. |
CROP: Zero-shot Cross-lingual Named Entity Recognition with Multilingual Labeled Sequence Translation (2022.findings-emnlp)
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Jian Yang, Shaohan Huang, Shuming Ma, Yuwei Yin, Li Dong, Dongdong Zhang, Hongcheng Guo, Zhoujun Li, Furu Wei
| Challenge: | Named entity recognition (NER) suffers from the scarcity of annotated training data, especially for low-resource languages without labeled data. |
| Approach: | They propose a cross-lingual entity projection framework to enable zero-shot cross-linguistic NER with the help of a multilingual labeled sequence translation model. |
| Outcome: | The proposed method outperforms the baseline method on two benchmarks by a large margin of +3 7 F1 scores and achieves state-of-the-art performance. |
Generation-driven Contrastive Self-training for Zero-shot Text Classification with Instruction-following LLM (2024.eacl-long)
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| Challenge: | a novel method to train a smaller model with LLMs for zero-shot text classification requires immense computational resources due to their substantial model size. |
| Approach: | They propose a method which leverages the generative power of large language models to train a smaller model. |
| Outcome: | The proposed method outperforms state-of-the-art methods when limited data is available. |
Uncertainty Estimation and Reduction of Pre-trained Models for Text Regression (2022.tacl-1)
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| Challenge: | State-of-the-art classification and regression models are often not well calibrated and can be inaccurate. |
| Approach: | They quantify calibration of pre- trained language models for text regression . they apply uncertainty estimates to augment training data in low-resource domains . |
| Outcome: | The proposed model calibrations improve performance and generalizability in low-resource settings. |
Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation (2023.acl-long)
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| Challenge: | Using self-training or text-to-speech (TTS) to improve low-resource ASR performance is costly and can lead to catastrophic forgetting. |
| Approach: | They examine whether data augmentation techniques could help improve low-resource ASR performance . they use self-training to generate transcriptions, which are combined with original data to train new system . |
| Outcome: | The proposed approach yields a 20.5% reduction in WER compared to a system trained on 24 minutes of manually transcribed speech. |
Rank-Aware Negative Training for Semi-Supervised Text Classification (2023.tacl-1)
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| Challenge: | Semi-supervised text classification-based paradigms employ the spirit of self-training, but the accuracy of pseudo-labels can be a problem in real-world scenarios. |
| Approach: | They propose a Rank-aware Negative Training framework to address SSTC in noisy label learning . they rank unlabeled texts based on evidential support from the labeled texts. |
| Outcome: | The proposed framework overcomes state-of-the-art alternatives and achieves competitive performance in other scenarios. |
Generate, Annotate, and Learn: NLP with Synthetic Text (2022.tacl-1)
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| Challenge: | Existing methods to generate unlabeled text are difficult to find. |
| Approach: | They propose a general framework called "generate, annotate, and learn" to take advantage of synthetic text within knowledge distillation, self-training, and few-shot learning applications. |
| Outcome: | The proposed framework achieves state-of-the-art knowledge distillation results for 6-layer transformers on the GLUE leaderboard. |
Unsupervised Bitext Mining and Translation via Self-Trained Contextual Embeddings (2020.tacl-1)
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| Challenge: | Existing methods to extract parallel sentences from unaligned text yield surprisingly good results. |
| Approach: | They propose an unsupervised method to create pseudo-parallel corpora for machine translation (MT) from unaligned text using multilingual BERT to create source and target sentence embeddings for nearest-neighbor search and adapt the model via self-training. |
| Outcome: | The proposed method outperforms existing methods and outperformed previous state-of-the-art methods by boosting translation performance by up to 3.5 BLEU on the WMT’14 French-English and WMT'16 German-English tasks. |
On the Use of External Data for Spoken Named Entity Recognition (2022.naacl-main)
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| Challenge: | Named entity recognition (NER) tasks require large labeled datasets to perform . compared to prior work, relative improvements in F1 of up to 16% are found . |
| Approach: | They propose to use self-training, knowledge distillation, and transfer learning to learn SLU models . they compare pipeline and pipeline approaches to find out how to use external data . |
| Outcome: | The proposed models improve performance beyond pre-trained models in resource-constrained settings . the best baseline model is a pipeline approach, while the best performance is achieved by an E2E model. |
Building Adaptive Acceptability Classifiers for Neural NLG (2021.emnlp-main)
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Soumya Batra, Shashank Jain, Peyman Heidari, Ankit Arun, Catharine Youngs, Xintong Li, Pinar Donmez, Shawn Mei, Shiunzu Kuo, Vikas Bhardwaj, Anuj Kumar, Michael White
| Challenge: | Existing approaches to generate synthetic data using simple sentence transformations and/or model-based techniques may not generate realistic error samples with respect to the NLG models. |
| Approach: | They propose a framework to train models to classify acceptability of responses generated by natural language generation models using a 2-stage approach . they use existing sentence transformations to generate samples that better resemble the output of the generation models. |
| Outcome: | The proposed approach outperforms existing techniques and can be used in few-shot settings using self-training. |
Self-Training Pre-Trained Language Models for Zero- and Few-Shot Multi-Dialectal Arabic Sequence Labeling (2021.eacl-main)
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| Challenge: | Existing approaches to fine-tune pre-trained language models for downstream tasks require labeled data. |
| Approach: | They propose to self-train pre-trained language models to improve performance on data-scarce varieties by as large as 10% F1 and 2% accuracy. |
| Outcome: | The proposed model improves zero-shot MSA-to-DA transfer by as large as 10% F1 (NER) and 2% accuracy (POS tagging). |
Self-Training with Weak Supervision (2021.naacl-main)
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| Challenge: | State-of-the-art deep neural networks require large amounts of labeled training data that is expensive to obtain or not available for many tasks. |
| Approach: | They propose a weak supervision framework that leverages all available data for a given task . they leverage task-specific unlabeled data through self-training with a model that predicts pseudo-labels for instances that may not be covered by weak rules . |
| Outcome: | The proposed framework improves on state-of-the-art datasets on six benchmark tasks. |
Detecting LLM-Assisted Cheating on Open-Ended Writing Tasks on Language Proficiency Tests (2024.emnlp-industry)
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| Challenge: | Large Language Models (LLMs) have been used for open-ended writing tasks . however, there are limitations in detecting LLM-generated samples . |
| Approach: | They propose a framework for training LLM-generated text detectors that can detect LLM generated samples after being copy-typed. |
| Outcome: | The proposed model outperforms the transformer-based classifier on a high-stakes online English proficiency test. |
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. |
Curriculum Prompt Learning with Self-Training for Abstractive Dialogue Summarization (2022.emnlp-main)
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| Challenge: | Existing methods to summarize dialogues are difficult due to insufficient training data and low information density. |
| Approach: | They propose a curriculum-based prompt learning method with self-training that gradually increases the degree of prompt perturbation, improving dialogue understanding and modeling capabilities. |
| Outcome: | The proposed model outperforms baseline models on the AMI and ICSI datasets and human evaluations show it is superior in the quality of the summary generation. |
Zero-Shot Text Classification with Self-Training (2022.emnlp-main)
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| Challenge: | Recent advances in large pretrained language models have increased attention to zero-shot text classification. |
| Approach: | They propose a plug-and-play method to bridge this gap by requiring only class names along with an unlabeled dataset. |
| Outcome: | The proposed model can be trained on a natural language inference dataset and performs on dozens of unseen tasks without the need for domain expertise or trial and error. |
Meta Self-Refinement for Robust Learning with Weak Supervision (2023.eacl-main)
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| Challenge: | Recent methods leverage self-training to build noise-resistant models . however, the teacher trained under weak supervision may have fitted a substantial amount of noise and therefore produce incorrect pseudo-labels. |
| Approach: | They propose a framework that encourages teacher to refine its pseudo-labels to effectively combat label noise from weak supervision. |
| Outcome: | The proposed framework outperforms state-of-the-art methods by 11.4% in accuracy and 9.26% in F1 score on eight NLP benchmarks. |
Lexically Aware Semi-Supervised Learning for OCR Post-Correction (2021.tacl-1)
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| Challenge: | Existing methods for digitizing text in endangered languages rely on manual data curated by the user. |
| Approach: | They propose a semi-supervised learning method that utilizes raw images to improve performance. |
| Outcome: | The proposed method reduces errors by 15%–29% on four endangered languages. |
SelfMix: Robust Learning against Textual Label Noise with Self-Mixup Training (2022.coling-1)
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| 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. |
Compositional Data Augmentation for Abstractive Conversation Summarization (2023.acl-long)
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| Challenge: | Abstractive conversation summarization systems rely on large-scale annotated summaries, but collecting and annotating these conversations can be time-consuming and labor-intensive. |
| Approach: | They propose a method for generating diverse and high-quality pairs of conversations and summaries by extracting conversation structures and organizing meaningful conversation snippets. |
| Outcome: | The proposed method outperforms baseline methods on SAMSum and DialogSum datasets and achieves a 10% increase in ROUGE scores with limited data. |
DaMSTF: Domain Adversarial Learning Enhanced Meta Self-Training for Domain Adaptation (2023.acl-long)
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| Challenge: | Existing approaches to domain adaptation only use reliable pseudo instances, i.e., pseudo instances with high prediction confidence, to retrain the model. |
| Approach: | They propose a domain adversarial learning enhanced self-training framework that uses meta-learning to estimate the importance of each pseudo instance and a meta constructor to construct the meta-validation set. |
| Outcome: | The proposed framework reduces label noise and preserves hard examples while maintaining accuracy. |
Co-training an Unsupervised Constituency Parser with Weak Supervision (2022.findings-acl)
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| Challenge: | Existing methods for unsupervised parsing that use bootstrapping classifiers to identify if a node dominates a span are lacking. |
| Approach: | They propose a method for unsupervised parsing that relies on bootstrapping classifiers to identify if a node dominates a specific span. |
| Outcome: | The proposed method achieves 63.1 F1 on the English test set and new state-of-the-art on treebanks for Chinese and Japanese. |
AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language Models (2022.naacl-main)
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| Challenge: | Existing methods for fine-tuning pre-trained language models ignore the potential of unlabeled data. |
| Approach: | They propose a framework that allows users to unleash the power of unlabeled data via self-training. |
| Outcome: | The proposed framework outperforms active learning and self-training baselines and improves the label efficiency of PLM fine-tuning by 56.2% on average. |
Stance Detection in COVID-19 Tweets (2021.acl-long)
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| Challenge: | a global pandemic of COVID-19 has forced major changes in our daily lives . a new stance detection dataset is being used to track the stances of Twitter users . |
| Approach: | They use Twitter stance data to collect stances on topics related to the pandemic . they train models to take advantage of large amounts of unlabeled data . |
| Outcome: | The proposed model improves on existing stance detection datasets and unlabeled data. |
The Source-Target Domain Mismatch Problem in Machine Translation (2021.eacl-main)
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Jiajun Shen, Peng-Jen Chen, Matthew Le, Junxian He, Jiatao Gu, Myle Ott, Michael Auli, Marc’Aurelio Ranzato
| Challenge: | Despite the interconnected world we live in, people in different places talk about different things in different parts of the world. |
| Approach: | They propose a metric to quantify the effect of local context in machine translation and propose measurable results. |
| Outcome: | The proposed metric can be used to quantify the effect of local context on the use of language in machine translation systems on low resource languages. |
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. |
Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection (2020.emnlp-main)
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| Challenge: | Existing approaches to disfluency detection rely on human annotations, which are expensive to obtain. |
| Approach: | They propose an unsupervised learning paradigm which can work with unlabeled text corpora. |
| Outcome: | The proposed method performs better than existing supervised systems using word embeddings. |
Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction (2021.emnlp-main)
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Mahsa Yarmohammadi, Shijie Wu, Marc Marone, Haoran Xu, Seth Ebner, Guanghui Qin, Yunmo Chen, Jialiang Guo, Craig Harman, Kenton Murray, Aaron Steven White, Mark Dredze, Benjamin Van Durme
| Challenge: | Zero-shot cross-lingual information extraction (IE) is a technique for training data in a source language but not in . |
| Approach: | They explore techniques including data projection and self-training to improve zero-shot cross-lingual information extraction (IE) IE is a construction of an IE model for some target language given existing annotations exclusively in English. |
| Outcome: | The proposed techniques show that they perform better than any single strategy. |
LiST: Lite Prompted Self-training Makes Parameter-efficient Few-shot Learners (2022.findings-naacl)
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| Challenge: | LiST is an efficient method for fine-tuning large pre-trained language models in few-shot learning settings. |
| Approach: | They propose a method for efficient fine-tuning of large pre-trained language models in few-shot settings using self-training and meta-learning. |
| Outcome: | The proposed method outperforms GPT-3 in-context learning by 33% on few-shot tasks. |
STAD: Self-Training with Ambiguous Data for Low-Resource Relation Extraction (2022.coling-1)
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| Challenge: | Existing approaches for low-resource relation extraction use only confident instances and uncertain instances. |
| Approach: | They propose a self-training approach for low-resource relation extraction using auto-annotated instances. |
| Outcome: | The proposed method improves on two widely used datasets with low-resource settings. |
Unsupervised Domain Adaptation for Question Generation with DomainData Selection and Self-training (2022.findings-naacl)
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| Challenge: | Existing question generation models require large-scale and high-quality training data. |
| Approach: | They propose an unsupervised domain adaptation approach to combat the lack of training data and domain shift issue with domain data selection and self-training. |
| Outcome: | The proposed approach outperforms baselines on three large datasets with different domain similarities, using a transformer-based pre-trained QG model. |
Multi-Domain Targeted Sentiment Analysis (2022.naacl-main)
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| Challenge: | Targeted Sentiment Analysis (TSA) is a task for generating insights from consumer reviews. |
| Approach: | They propose a multi-domain TSA system that augments a given training set with diverse weak labels from assorted domains and augments it with Yelp reviews. |
| Outcome: | The proposed model outperforms manual methods on three evaluation datasets across different domains and shows that it performs well. |
A Scalable Framework for Automated NER Annotation Correction in Low-Resource Languages (2026.findings-eacl)
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| Challenge: | Existing NER benchmarks lack quality annotations, resulting in poor performance. |
| Approach: | They propose a frequency-based iterative approach that leverages self-training and a dual-threshold mechanism to enhance inference confidence. |
| Outcome: | The proposed approach improves NER performance on three datasets with a high number of missing annotations. |
Learning from Executions for Semantic Parsing (2021.naacl-main)
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| Challenge: | Semantic parsing aims at translating natural language (NL) utterances onto machine-interpretable programs. |
| Approach: | They propose to encourage a parser to generate executable programs for unlabeled NL utterances. |
| Outcome: | The proposed training objectives outperform conventional methods on Overnight and GeoQuery. |
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. |
Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning (2023.acl-long)
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| Challenge: | Existing methods to bridge the linguistic gap between self-training and monolingual named entity recognition (NER) however, due to sub-optimal performance on target languages, the pseudo labels are noisy and limit the overall performance. |
| Approach: | They propose to combine representation learning and pseudo label refinement in one coherent framework to improve self-training for cross-lingual named entity recognition (NER) |
| Outcome: | The proposed method improves cross-lingual named entity recognition (NER) on multiple transfer pairs. |
Third-Party Aligner for Neural Word Alignments (2022.findings-emnlp)
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| Challenge: | Existing work shows that word alignment can be competitive . |
| Approach: | They propose to use word alignments generated by a third-party word aligner to supervise the neural word alignment training. |
| Outcome: | The proposed approach can find more accurate word alignments and delete wrong alignments, leading to better performance than the current best third-party word aligner. |
PPT: Parsimonious Parser Transfer for Unsupervised Cross-Lingual Adaptation (2021.eacl-main)
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| Challenge: | Existing methods for cross-lingual transfer use implicit supervision to parse low-resource languages without explicit supervision. |
| Approach: | They propose a method for unsupervised cross-lingual transfer that uses their output as implicit supervision as part of self-training on unlabelled text in the target language. |
| Outcome: | The proposed method improves over state-of-the-art models on both distant and nearby languages, despite being conceptually simpler. |
MetaTS: Meta Teacher-Student Network for Multilingual Sequence Labeling with Minimal Supervision (2021.emnlp-main)
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| Challenge: | Sequence labeling aims to predict fine-grained sequences of labels for text, but lack of token-level annotated data hinders the effectiveness of supervised methods. |
| Approach: | They propose a Meta Teacher-Student (MetaTS) Network to alleviate data scarcity by leveraging large multilingual unlabeled data. |
| Outcome: | The proposed meta learning method alleviates data scarcity by leveraging large multilingual unlabeled data. |
Self-training Reduces Flicker in Retranslation-based Simultaneous Translation (2023.eacl-main)
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| Challenge: | Existing approaches to reduce flicker in simultaneous translation have increased the latency through masking and specialised inference, thus losing the simplicity of the approach. |
| Approach: | They propose to train a machine translation system to reduce flicker by controlling monotonicity and biased beam search to achieve the same flicker-latency tradeoff. |
| Outcome: | The proposed approach reduces flicker by controlling monotonicity while maintaining similar translation quality to the original. |
HS-STaR: Hierarchical Sampling for Self-Taught Reasoners via Difficulty Estimation and Budget Reallocation (2025.emnlp-main)
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| Challenge: | Recent studies have incorporated reward models to guide response selection or decoding, aiming to obtain higher-quality data. |
| Approach: | They propose a Hierarchical Sampling framework for self-taught reasoners that allocates a fixed sampling budget to problem boundary-level problems and then reallocates the remaining budget toward high-utility problems during a re-sampling phase. |
| Outcome: | The proposed framework outperforms baseline models without additional sampling budgets across multiple reasoning benchmarks and backbone LLMs. |
EICO: Improving Few-Shot Text Classification via Explicit and Implicit Consistency Regularization (2022.findings-acl)
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| Challenge: | Existing methods for few-shot text classification are limited by labeled data. |
| Approach: | They propose to use consistency regularization to improve few-shot text classification by generating pseudo-labels from weakly-augmented and strongly-augmented views. |
| Outcome: | The proposed method achieves competitive performance with 16 labeled examples with prompt and verbalizer. |
Improving Compositional Generalization with Self-Training for Data-to-Text Generation (2022.acl-long)
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| Challenge: | Data-to-text generation focuses on generating fluent natural language responses from structured meaning representations (MRs). |
| Approach: | They propose a template-based input representation that greatly improves the model’s generalization capability. |
| Outcome: | The proposed model improves tree accuracy by 46%+ and reduces slot error rates by 73%+ over the strong baselines on SGD and Weather benchmarks. |
Self-Reasoning Language Models: Unfold Hidden Reasoning Chains with Few Reasoning Catalyst (2025.findings-acl)
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| Challenge: | Recent studies have demonstrated that inference-time scaling increases performance of Large Language Models (LLMs) in various reasoning tasks such as mathematics and complex question answering by increasing the length of Chain-of-Thought (CoT). |
| Approach: | They propose a model which synthesizes longer CoT data and iteratively improves performance through self-training by incorporating a few demonstration examples. |
| Outcome: | The proposed model achieves an average improvement of more than +2.5 points across five reasoning tasks: MMLU, GSM8K, ARC-C, HellaSwag, and BBH on two backbone models. |
Few Shot Rationale Generation using Self-Training with Dual Teachers (2023.findings-acl)
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| Challenge: | Existing models that generate free-text explanations for annotated labels are expensive and require a large annotation dataset. |
| Approach: | They propose a self-training approach leveraging both labeled and unlabeled data to further improve few-shot models by combining teacher models and a multi-tasking student model. |
| Outcome: | The proposed model improves on three public datasets and can generate a free-text explanation for predicted labels. |
ZARA: Improving Few-Shot Self-Rationalization for Small Language Models (2023.findings-emnlp)
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| Challenge: | Recent studies demonstrate great performance gain for self-rationalization by few-shot prompting LMs with rationale-augmented exemplars. |
| Approach: | They propose to leverage explanations for small LMs to improve few-shot self-rationalization by reducing the problem of plausibility judgement to natural language inference. |
| Outcome: | The proposed approach achieves SOTA performance on the FEB benchmark, for both the task accuracy and the explanation metric. |
CATE: A Contrastive Pre-trained Model for Metaphor Detection with Semi-supervised Learning (2021.emnlp-main)
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| Challenge: | Existing models for metaphor detection require a large amount of labeled data and are not linguistically-based. |
| Approach: | They propose a ContrAstive pre-Trained modEl (CATE) for metaphor detection with semi-supervised learning using a pre-trained model to obtain a contextual representation of target words. |
| Outcome: | The proposed model outperforms existing models on several benchmark datasets and achieves better performance against state-of-the-art models. |
Improving Disfluency Detection by Self-Training a Self-Attentive Model (2020.acl-main)
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| Challenge: | Existing self-attentive parsers using contextualized word embeddings produce state-of-the-art results in joint parsing and disfluency detection. |
| Approach: | They propose to use contextualized word embeddings to train a neural model using unlabeled data to train parsers. |
| Outcome: | The proposed method produces state-of-the-art results in parsing and disfluency detection in speech transcripts. |
Delta-training: Simple Semi-Supervised Text Classification using Pretrained Word Embeddings (D19-1)
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| Challenge: | Pretrained word embeddings outperforms classifiers with randomly initialized word embeds, a new method is proposed for semi-supervised text classification. |
| Approach: | They propose a method that uses pretrained word embeddings to predict text classification . they use unlabeled data to build a classifier, and use early-stopping to improve performance . |
| Outcome: | The proposed method outperforms self-training and co-training frameworks on unlabeled data. |
Borrowing Human Senses: Comment-Aware Self-Training for Social Media Multimodal Classification (2022.emnlp-main)
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| Challenge: | Social media users are using images and text to voice opinions and share ideas. |
| Approach: | They propose to use user comments to extract hinting features from user comments and explore them via self-training. |
| Outcome: | The proposed framework improves on four social media benchmarks for image-text relation classification, sarcasm detection, sentiment classification, and hate speech detection. |
Improving Low-resource RRG Parsing with Cross-lingual Self-training (2022.coling-1)
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| Challenge: | a theoretical framework for low-resource parsing is understudied in computational linguistics but widely used in typological research . a novel approach uses Role and Reference Grammar to parse low-source languages . |
| Approach: | They propose to extend an existing RRG parser into a cross-lingual parsing model . they also adopt self-training to adapt the model to a related language with no trees . |
| Outcome: | The proposed model extends into a cross-lingual parser, and iteratively expands the training data. |
Leveraging the Structure of Pre-trained Embeddings to Minimize Annotation Effort (2024.naacl-long)
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| Challenge: | Current approaches for text classification are based on fine-tuning the representations computed by large language models. |
| Approach: | They propose to exploit structural properties of pre-trained embeddings to spread information . they use a semisupervised strategy to train models with minimal annotation effort . |
| Outcome: | The proposed method outperforms self-training and random walk labels on different datasets. |
Self-Training using Rules of Grammar for Few-Shot NLU (2021.findings-emnlp)
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| Challenge: | Existing methods for learning natural language understanding are limited in low-resource settings. |
| Approach: | They propose to use rules of grammar to construct and expand rules of grammatical structure of data without human involvement. |
| Outcome: | The proposed approach outperforms state-of-the-art methods in three benchmark datasets. |
Semi-Supervised Text Classification with Balanced Deep Representation Distributions (2021.acl-long)
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| Challenge: | Semi-Supervised Text Classification (SSTC) is a type of self-training that uses labeled and unlabeled data to perform certain applications. |
| Approach: | They propose a method to initialize a deep classifier by training over labeled texts . they then alternatively predict unlabeled texts as their pseudo-labels and train them over the mixture . |
| Outcome: | Empirical results show that the proposed method is more accurate when labeled texts are scarce. |
Extreme Zero-Shot Learning for Extreme Text Classification (2022.naacl-main)
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| Challenge: | Experimental results show that MACLR achieves superior performance compared to other baseline methods. |
| Approach: | They propose to pre-train Transformer-based encoders with self-supervised contrastive losses to learn the semantic embeddings of instances and labels with raw text. |
| Outcome: | The proposed method improves on the EZ-XMC model with a limited number of ground-truth positive pairs. |
Unsupervised Domain Adaptation for Text Classification via Meta Self-Paced Learning (2022.coling-1)
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| Challenge: | Recent methods addressing unsupervised domain adaptation for textual tasks extracted domain-invariant representations through balancing between multiple objectives to align feature spaces between source and target domains. |
| Approach: | They propose to use meta-learning framework to train a neural network-based self-paced learning procedure in an end-to-end manner. |
| Outcome: | The proposed method significantly improves performance on target domains, surpassing state-of-the-art approaches. |
Self-training Improves Pre-training for Natural Language Understanding (2021.naacl-main)
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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. |
Learning to Detect Noisy Labels Using Model-Based Features (2022.findings-emnlp)
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Zhihao Wang, Zongyu Lin, Junjie Wen, Xianxin Chen, Peiqi Liu, Guidong Zheng, Yujun Chen, Zhilin Yang
| Challenge: | Existing approaches to reduce label noise rely on heuristics and sample losses. |
| Approach: | They propose a method that transfers the noise distribution to a clean set and trains a model to distinguish noisy labels from clean ones using model-based features. |
| Outcome: | Empirically, the proposed approach improves over strong baselines on a wide range of tasks including text classification and speech recognition. |
Fine-grained Entity Typing without Knowledge Base (2021.emnlp-main)
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| Challenge: | Existing work on fine-grained entity typing (FET) relies on knowledge bases as distant supervision, but lack of or incompleteness of KB can hinder training. |
| Approach: | They propose a two-step framework that trains FET models without accessing any knowledge base. |
| Outcome: | The proposed framework achieves competitive performance with respect to the models trained on the original KB-supervised datasets. |
LLMs Deceive Unintentionally: Emergent Misalignment in Dishonesty from Misaligned Samples to Biased Human-AI Interactions (2026.findings-acl)
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| Challenge: | Existing studies have shown that LLMs finetuned on incorrect completions can exhibit harmful behaviors, which is called emergent misalignment. |
| Approach: | They investigate whether LLMs finetuned on incorrect completions can exhibit harmful behaviors . they find that 1% of misalignment data is sufficient to decrease honest behavior . |
| Outcome: | The proposed model can be misaligned on errors within narrow domains to exhibit harmful behaviors . the proposed model is able to exhibit dishonest behavior with only 10% biased user population . |
STraTA: Self-Training with Task Augmentation for Better Few-shot Learning (2021.emnlp-main)
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| Challenge: | Recent advances in NLP demonstrate the effectiveness of applying large-scale pre-trained language models to downstream tasks. |
| Approach: | They propose a method that uses task augmentation to fine-tune unlabeled data. |
| Outcome: | The proposed approach improves sample efficiency across 12 few-shot benchmarks. |
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 . |
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. |
Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding (2023.findings-emnlp)
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| Challenge: | Existing methods for pre-trained language models rely on noisy data, which can be expensive if all parameters are updated. |
| Approach: | They propose a self-training framework that incorporates Monte Carlo dropouts into the model and judiciously selects reliable pseudo-labeled examples based on confidence and certainty. |
| Outcome: | The proposed framework improves performance and efficiency over multiple tasks over multiple datasets. |
VEEF-Multi-LLM: Effective Vocabulary Expansion and Parameter Efficient Finetuning Towards Multilingual Large Language Models (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have a significant disadvantage for low-resource languages . VEEF-Multi-LLM-8B excels in multilingual instruction-following tasks . |
| Approach: | They propose a low-resource multilingual large language model that expands the vocabulary for multilingual support. |
| Outcome: | The proposed model outperforms existing models in multilingual instruction-following tasks, but lags behind English-centric models in some tasks. |
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. |
Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval (2021.emnlp-main)
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| Challenge: | Using self-training to train unsupervised domains can be expensive, resulting in poor generalization due to distributional shift. |
| Approach: | They propose to use unaligned data to train unsupervised domain adaptation models using cheap synthetically generated labeled data. |
| Outcome: | The proposed method significantly outperforms self-training on question generation and passage retrieval domains and on MLQuestions and PubMedQA. |
A Comparison of Strategies for Source-Free Domain Adaptation (2022.acl-long)
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| Challenge: | Existing research on domain adaptation without access to training data is limited due to privacy concerns. |
| Approach: | They compare active learning, self-training, and data augmentation strategies for source-free domain adaptation with a shared task. |
| Outcome: | The proposed algorithms yield consistent gains across all SemEval 2021 Task 10 tasks and domains, but they are unreliable for source-free domain adaptation. |
GiFT: Gibbs Fine-Tuning for Code Generation (2025.acl-long)
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| Challenge: | Training Large Language Models (LLMs) with synthetic data is a prevalent practice in code generation. |
| Approach: | They propose a method to fine-tune large language models with code drawn from a conditional distribution, conditioned on a specific seed description. |
| Outcome: | The proposed method improves performance on four datasets and shows that it can be used to fine-tune LLMs with code derived from the marginal distribution. |
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. |
On the Role of Supervision in Unsupervised Constituency Parsing (2020.emnlp-main)
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| Challenge: | Recent work on unsupervised constituency parsing uses labeled examples for tuning . a few-shot parser with labeles can outperform other approaches by a significant margin . |
| Approach: | They propose to use as few labeled examples as possible for model development . they propose to train existing models on the same labeles they access . |
| Outcome: | The proposed model outperforms other models on the WSJ development set by a significant margin . the proposed model can be further improved by augmentation and self-training . |
Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models (2025.acl-long)
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| Challenge: | Existing methods to fine-tune Large Language Models without human annotations are lacking in the field of natural language training. |
| Approach: | They propose an environment-guided neural-symbolic self-training framework to overcome two main challenges: the scarcity of symbolic data and the limited proficiency of LLMs in processing symbolic language. |
| Outcome: | The proposed framework overcomes two main challenges: the scarcity of symbolic data, and the limited proficiency of LLMs in processing symbolic language. |
Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction (2024.acl-long)
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| Challenge: | Aspect Sentiment Quad Prediction (ASQP) aims to predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review. |
| Approach: | They propose a self-training framework with a pseudo-label scorer to assess the match between reviews and their pseudo-labels and train a generative model on it. |
| Outcome: | The proposed framework can predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review, and it can significantly improve self-training. |
Self-Training with Direct Preference Optimization Improves Chain-of-Thought Reasoning (2024.acl-long)
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| Challenge: | Recent studies focus on enhancing large-scale language models' reasoning abilities, but the research question of how to GSM8K Performance vs. computational cost remains. |
| Approach: | They propose to train small-scale language models with their own outputs to avoid relying on large models' outputs. |
| Outcome: | The proposed approach outperforms baseline models with comparable sizes while minimizing the required compute. |
Learning from a Friend: Improving Event Extraction via Self-Training with Feedback from Abstract Meaning Representation (2023.findings-acl)
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| Challenge: | Existing data scarcity hinders the progress of event extraction, authors say . ACE-052 has 10 of the 33 event types with less than 80 annotations, authors claim . |
| Approach: | They propose a self-training with feedback framework that leverages large-scale unlabeled data to acquire feedback for each new event prediction from the unlabed data. |
| Outcome: | The proposed framework improves event extraction models even when unlabeled data are unavailable. |
Self-Training for Sample-Efficient Active Learning for Text Classification with Pre-Trained Language Models (2024.emnlp-main)
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| 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. |
Towards Open Domain Event Trigger Identification using Adversarial Domain Adaptation (2020.acl-main)
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| Challenge: | supervised event trigger identification models can generalize better across domains . prior work focused on annotating specific categories of events or narratives from specific domains. |
| Approach: | They propose to use adversarial domain adaptation framework to build supervised event trigger identification models which can generalize better across domains. |
| Outcome: | The proposed model improves on literature and news domains with no labeled data. |
OpineSum: Entailment-based self-training for abstractive opinion summarization (2023.findings-acl)
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| Challenge: | Abstractive summarization is promising for fluently comparing opinions from a set of reviews about a place or product. |
| Approach: | They propose a novel method that automatically leverages common opinions across reviews to create powerful abstractive models. |
| Outcome: | The proposed method outperforms strong peer systems in both settings. |
A Class-Rebalancing Self-Training Framework for Distantly-Supervised Named Entity Recognition (2023.findings-acl)
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| Challenge: | Distant supervision reduces the reliance on human annotation in named entity recognition tasks. |
| Approach: | They propose a class-rebalancing self-training framework for improving distantly-supervised named entity recognition by using a flexible threshold and a hybrid pseudo label. |
| Outcome: | The proposed model achieves state-of-the-art on five flat and two nested datasets and compares with other methods on the same dataset. |
Text Classification Using Label Names Only: A Language Model Self-Training Approach (2020.emnlp-main)
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| Challenge: | Current text classification methods require a large number of labeled documents as training data. |
| Approach: | They propose a model that uses only the label name of each class to train classification models on unlabeled data without using any labeled examples. |
| Outcome: | The proposed model achieves 90% accuracy on four benchmark datasets using label names as the only supervision . |
Neural Networks Against (and For) Self-Training: Classification with Small Labeled and Large Unlabeled Sets (2023.findings-acl)
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| Challenge: | Existing models for text classification suffer from the semantic drift problem, which is a problem for self-training. |
| Approach: | They propose a semi-supervised text classifier based on self-training using one positive and one negative property of neural networks. |
| Outcome: | The proposed model outperforms ten baseline models in five benchmarks and is additive to language model pretraining. |
Improving Cross-lingual Transfer with Contrastive Negative Learning and Self-training (2024.lrec-main)
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| Challenge: | Recent studies improve cross-lingual transfer learning by better aligning the internal representations within the multilingual model or exploring the information of the target language using self-training. |
| Approach: | They propose to use negative pairs to align the multilingual model and self-train the model to converge on the obtained clean pseudo-labels. |
| Outcome: | The proposed method improves upon the baseline models and can serve as a beneficial complement to the alignment-based methods. |
Entailment as Robust Self-Learner (2023.acl-long)
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| Challenge: | Recent studies have found that entailment pretraining benefits weakly supervised fine-tuning. |
| Approach: | They propose a prompting strategy that formulates different NLU tasks as contextual entailment and propose an algorithm for better pseudo-labeling quality in self-training. |
| Outcome: | The proposed approach improves the zero-shot adaptation performance on downstream tasks. |
Self-Training Large Language Models with Confident Reasoning (2025.findings-emnlp)
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| Challenge: | Large language models generate reasoning paths before final answers, but learning such a path requires costly human supervision. |
| Approach: | They propose a method that fine-tunes LLMs to prefer reasoning paths with high confidence . they propose 'cORE-PO' that fine tunes Lms to choose high-quality reasoning paths . |
| Outcome: | The proposed method improves the accuracy of outputs on four in-distribution and two out-of-difference benchmarks. |
Few-Shot Named Entity Recognition: An Empirical Baseline Study (2021.emnlp-main)
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Jiaxin Huang, Chunyuan Li, Krishan Subudhi, Damien Jose, Shobana Balakrishnan, Weizhu Chen, Baolin Peng, Jianfeng Gao, Jiawei Han
| Challenge: | Existing methods to build named entity recognition systems with limited labeled data are lacking. |
| Approach: | They propose three orthogonal schemes to build named entity recognition systems when labeled data is limited. |
| Outcome: | The proposed NER systems outperform existing methods on few-shot and training-free settings. |
Q-PRM: Adaptive Query Rewriting for Retrieval-Augmented Generation via Step-level Process Supervision (2025.findings-emnlp)
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| Challenge: | Existing approaches to rewriting queries often lack supervision signals for intermediate steps . existing approaches rely on outcome-supervised training or heuristic rules to guide the rewrite process . |
| Approach: | They propose a query rewriting framework that generates process-level supervision signals for intermediate steps. |
| Outcome: | a new query rewriting framework outperforms existing approaches on open-domain QA benchmarks. |
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. |
Data-efficient Active Learning for Structured Prediction with Partial Annotation and Self-Training (2023.findings-emnlp)
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| Challenge: | Structured prediction is a fundamental problem in NLP, wherein the label space consists of complex structured outputs with groups of interdependent variables. |
| Approach: | They propose a partial annotation approach that selects only the most informative sub-structures for annotation and a method that incorporates the current model's automatic predictions as pseudo-labels for un-annotated sub-structurals. |
| Outcome: | The proposed approach reduces annotation cost over strong full annotation baselines under a fair comparison scheme that takes reading time into consideration. |
ReflectEvo: Improving Meta Introspection of Small LLMs by Learning Self-Reflection (2025.findings-acl)
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Jiaqi Li, Xinyi Dong, Yang Liu, Zhizhuo Yang, Quansen Wang, Xiaobo Wang, Song-Chun Zhu, Zixia Jia, Zilong Zheng
| Challenge: | ReflectEvo-460k is a large-scale, comprehensive, self-generated reflection dataset with broadened instructions and diverse multi-domain tasks. |
| Approach: | They propose a pipeline that iteratively generates self-reflection for self-training and a large-scale reflection dataset with broadened instructions and diverse multi-domain tasks. |
| Outcome: | The proposed pipeline improves Llama-3 reasoning ability by up to 71.2% and Mistral by upto 44.4%. |
Improving Word Alignment Using Semi-Supervised Learning (2025.findings-acl)
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| Challenge: | Existing word alignment methods rely on labeled data, but augmenting training with pseudo-labeled data improves performance. |
| Approach: | They propose a semi-supervised framework to improve word alignment methods . they use pseudo-labeled data from multilingual encoder models as word aligners . |
| Outcome: | The proposed framework outperforms the current state-of-the-art binary alignment method on word alignment datasets. |
SuperST: Superficial Self-Training for Few-Shot Text Classification (2024.lrec-main)
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| Challenge: | In few-shot text classification, self-training relies on pseudo-labels to expand data, which has shown success, but can accumulate errors due to noisy pseudo-labeled data. |
| Approach: | They propose a method to mitigate noise in noisy pseudo-labeled data by applying superficial learning to noisy data and fine-tuning to less noisy data. |
| Outcome: | The proposed framework improves the classifier accuracy for few-shot text classification by 18.5% at most and 8% in average, compared with the state-of-the-art SSL baselines. |
GRAT: Guiding Retrieval-Augmented Reasoning through Process Rewards Tree Search (2025.acl-long)
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| Challenge: | Existing methods to enhance large models for multi-hop question-answering lack the ability for multipath exploration, strategic look-ahead, stepwise evaluation, and global selection. |
| Approach: | They propose an algorithm guided by Monte Carlo Tree Search and process rewards that assigns fine-grained rewards to each step in the search path. |
| Outcome: | The proposed algorithm outperforms various RAG-based methods on four multihop QA datasets and shows that it can self-train and self-update. |
Rationalize and Align: Enhancing Writing Assistance with Rationale via Self-Training for Improved Alignment (2025.findings-acl)
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| Challenge: | Existing writing assistants rely on supervised fine-tuning to optimize models for multiple revisions. |
| Approach: | They propose a framework that enhances WA performance with rationale and alignment. |
| Outcome: | The proposed framework outperforms state-of-the-art WAs and the closed-source GPT-4o by 3.9 and 7.1 points on average across eight well-established writing-related test sets. |
microCLIP: Unsupervised CLIP Adaptation via Coarse-Fine Token Fusion for Fine-Grained Image Classification (2026.findings-acl)
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| Challenge: | Existing UA methods for fine-grained image classification rely on coarse-grain visual tokens, which misses fine spatial details. |
| Approach: | They propose a label-free self-training framework that adapts visual features and LLMderived text prototypes using fine-grained cues. |
| Outcome: | The proposed framework improves alignment between finegrained visual regions and rich textual descriptions while updating only layer norms and a tiny head. |
Beyond Static Alignment: Adaptive Arbitration for Semantic Incongruence in Semi-Supervised Multimodal Sentiment Analysis (2026.acl-long)
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| Challenge: | Existing methods for semantic incongruence in sentiment analysis are limited by label-limited settings. |
| Approach: | They propose a framework for semi-supervised multimodal sentiment analysis that emphasizes stable cross-modal representations and reliable supervision. |
| Outcome: | The proposed framework outperforms state-of-the-art methods under label-limited settings. |
PRISMA: Preference-Reinforced Self-Training Approach for Interpretable Emotionally Intelligent Negotiation Dialogues (2026.acl-long)
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| Challenge: | Emotion plays a pivotal role in shaping negotiation outcomes, influencing trust, cooperation, and long-term relationships. |
| Approach: | They propose an Emotion-aware Negotiation Strategy-informed Chain-of-Thought reasoning mechanism which mimics human negotiation by perceiving, understanding, using, and managing emotions. |
| Outcome: | The proposed system generates interpretable emotions and improves negotiation effectiveness on job interviews and resource allocation datasets. |