Papers with mutual learning
Cooperative Denoising for Distantly Supervised Relation Extraction (C18-1)
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| Challenge: | Existing methods for distantly supervised relation extraction suffer from noisy labeling problem, which can severely degrade its performance. |
| Approach: | They propose a framework for distantly supervised relation extraction that leverages text corpus and knowledge graph and a cooperative module involving their mutual learning. |
| Outcome: | The proposed method reduces the noisy labels and achieves substantial improvement over the state-of-the-art methods. |
L4: Mutual Learning Helps Lifelong Language Learning (2025.emnlp-industry)
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| Challenge: | Existing distillation methods rely on domain-specific teachers, limiting their ability to update in real-time and adapt to dynamic environments. |
| Approach: | They propose a framework that enables continuous mutual learning from task streams without relying on domain-specific teachers. |
| Outcome: | The proposed framework reduces catastrophic forgetting while improving performance on various benchmark datasets making it suitable for real-world, dynamic natural language processing (NLP) applications. |
Multi-Agent Mutual Learning at Sentence-Level and Token-Level for Neural Machine Translation (2020.findings-emnlp)
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| Challenge: | Neural machine translation (NMT) has achieved significant progress over recent years. |
| Approach: | They extend mutual learning to the machine translation task and operate at both the sentence-level and the token-level. |
| Outcome: | The proposed method improves on the IWSLT’14 German-English task and also on the WMT’14 English-German task. |
Exploring All-In-One Knowledge Distillation Framework for Neural Machine Translation (2023.emnlp-main)
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| Challenge: | Existing knowledge distillation methods only obtain one lightweight student each time . this could be resource-intensive and resulting in multiple students not being optimally utilized . |
| Approach: | They propose a knowledge distillation framework which generates multiple satisfactory students at once. |
| Outcome: | The proposed framework generates multiple satisfactory students at once. |
GAML-BERT: Improving BERT Early Exiting by Gradient Aligned Mutual Learning (2021.emnlp-main)
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| Challenge: | Existing approaches to improve the early exiting of natural language processing (NLP) are notoriously gigantic and slow in both training and inference. |
| Approach: | They propose a framework for improving the early exiting of BERT by asking each exit to distill knowledge from each other. |
| Outcome: | The proposed framework outperforms the state-of-the-art (SOTA) BERT early exiting methods on the GLUE benchmark. |
Logical Consistency as a Bridge: Improving LLM Hallucination Detection via Label Constraint Modeling between Responses and Self-Judgments (2026.acl-long)
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Hao Mi, Qiang Sheng, Shaofei Wang, Beizhe Hu, Yifan Sun, Zhengjia Wang, Hengqi Zeng, Yang Li, Danding Wang, Juan Cao
| Challenge: | Existing methods for hallucination detection focus on implicit neural uncertainty or explicit symbolic reasoning, ignoring factual hallucinosities. |
| Approach: | They propose a framework that bridges neural features and symbolic judgments for hallucination detection by leveraging a "meta-judgment" process to map symbolic labels back into the feature space. |
| Outcome: | Extensive experiments on 4 public datasets, across 4 LLMs, against 8 baselines demonstrate the superiority of LaaB. |
ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding (2023.findings-acl)
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| Challenge: | Despite efforts to improve ASR robustness, errors from pipeline approaches can lead to error propagation. |
| Approach: | They propose a framework for improving ASR robustness in SLU by using mutual learning and large-margin contrastive learning. |
| Outcome: | The proposed framework outperforms existing models and achieves new state-of-the-art performance on three datasets. |
JointMatch: A Unified Approach for Diverse and Collaborative Pseudo-Labeling to Semi-Supervised Text Classification (2023.emnlp-main)
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| Challenge: | Existing approaches to semi-supervised text classification suffer from pseudo-label bias and error accumulation. |
| Approach: | They propose a pseudo-labeling approach to semi-supervised text classification that unifies ideas from semi-semi-supervised learning and the task of learning with noise. |
| Outcome: | The proposed approach achieves a significant improvement on benchmark datasets even in the extremely-scarce-label setting. |
Dual Capsule Attention Mask Network with Mutual Learning for Visual Question Answering (2022.coling-1)
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| Challenge: | Visual Question Answering (VQA) models extract features from images and questions independently, but these methods fail to capture fine-grained key features and include much unnecessary information. |
| Approach: | They propose a dual capsule attention mask network with mutual learning for visual question answering (VQA) it contains two branches processing coarse-grained features and fine-grain features, respectively. |
| Outcome: | The proposed model outperforms baselines in terms of performance and interpretability and achieves new SOTA performance on the VQA-v2 dataset. |
Iterative Dual-Model Alignment for Story Evaluation (2026.acl-long)
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| Challenge: | Existing evaluators of large language models are static and lack the ability to refine their reasoning through interaction. |
| Approach: | They propose an Alpha–Beta Learning framework that trains two complementary 8B models: an Alpha classifier that assesses pairwise story engagement, and a Beta generator that produces structured, rubric-guided comparative explanations. |
| Outcome: | The proposed framework outperforms strong single-model baselines on human-annotated story-pair datasets in both accuracy and explanation quality across multiple iterative rounds. |
Towards Multi-modal Sarcasm Detection via Disentangled Multi-grained Multi-modal Distilling (2024.lrec-main)
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| Challenge: | Existing approaches to sarcasm detection focus on textual and intra-modal incongruity . mainstream approaches process input of each modality in a holistic manner, resulting in redundant and unrefined information. |
| Approach: | They propose a framework for multi-modal sarcasm detection that disentangles modality representations into latent spaces and conducts multi-grained knowledge distilling. |
| Outcome: | The proposed framework overpowers existing methods on a common benchmark. |
Unifying Latent and Lexicon Representations for Effective Video-Text Retrieval (2024.lrec-main)
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Haowei Liu, Yaya Shi, Haiyang Xu, Chunfeng Yuan, Qinghao Ye, Chenliang Li, Ming Yan, Ji Zhang, Fei Huang, Bing Li, Weiming Hu
| Challenge: | Existing methods for video-text retrieval capture fine-grained semantic concepts . however, they lack the ability to capture finer-grain concepts such as objects and actions. |
| Approach: | They propose a dual-encoder architecture for fast video-text retrieval that learns lexicon representations to capture fine-grained semantics. |
| Outcome: | The proposed framework outperforms existing methods with 4.8% and 8.2% improvement on MSR-VTT and DiDeMo respectively. |