Papers with co-training
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. |
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. |
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. |
MultiMatch: Multihead Consistency Regularization Matching for Semi-Supervised Text Classification (2025.emnlp-main)
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| Challenge: | **MultiMatch** is a semi-supervised learning (SSL) algorithm that combines co-training and consistency regularization with pseudo-labeling. |
| Approach: | They propose a semi-supervised learning algorithm that integrates co-training and consistency regularization with pseudo-labeling. |
| Outcome: | The proposed algorithm outperforms the second-best approach on 8 out of 10 setups from 5 natural language processing datasets and outperformed the second best by 3.26%. |
Human Needs Categorization of Affective Events Using Labeled and Unlabeled Data (N18-1)
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| Challenge: | Recent research has focused on identifying affective events in text, which are activities or states that positively or negatively affect the people who experience them. |
| Approach: | They propose to categorize affective events based upon human need categories that often explain people’s motivations and desires: PHYSIOLOGICAL, HEALTH, LEISURE, SOCIAL, FINANCIAL, COGNITION, and FREEDOM. |
| Outcome: | The proposed model learns from unlabeled data and produces significantly better results than individual classifiers. |
Co-training and Co-distillation for Quality Improvement and Compression of Language Models (2023.findings-emnlp)
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| Challenge: | Knowledge Distillation (KD) compresses expensive pre-trained language models . however, most smaller models fail to surpass performance of larger model . |
| Approach: | They propose a framework that co-trains two models while mutually distilling knowledge to improve performance and inference speed together. |
| Outcome: | The proposed framework outperforms the original larger model by 1.66 on the GLUE benchmark. |
Effective Fine-Tuning Methods for Cross-lingual Adaptation (2021.emnlp-main)
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| Challenge: | Large scale multilingual pre-trained language models have shown promising results in zero- and few-shot cross-lingual tasks. |
| Approach: | They propose a co-tuning method that aims to learn more generalized semantic equivalences when the languages are structurally dissimilar. |
| Outcome: | The proposed method improves on cross-lingual inference and review tasks by capturing the semantic relationship in the parallel data when a few translation pairs are available. |
Rhombus: Incentivizing Coordination in Parallel Thinking through Reinforcement Learning (2026.findings-acl)
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Ziyuan Nan, Qi Yi, Di Huang, Yutong Wu, Guanhua Huang, Xue Gong, Kejiao Li, Yuhao Jiang, Chenchen Zhang, Zenan Xu, Xing Hu, Bo Zhou
| Challenge: | Parallel thinking is a promising avenue for scaling test-time compute in Large Language Models . however, coordinating the exploration and aggregation stages remains challenging . |
| Approach: | They propose a parallel thinking framework that explicitly incentivizes coordination between components via end-to-end reinforcement learning. |
| Outcome: | The proposed framework improves accuracy by 6.0% over long chain-of-thought baselines while reducing wall-clock latency by 39.4% under matched token budgets. |