Papers with weakly supervised learning
A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation (2021.emnlp-main)
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| Challenge: | Existing knowledge-grounded dialogues perform poorly when transfer into new domains with limited training samples. |
| Approach: | They propose a weakly supervised three-stage learning framework based on weakly-supervised learning based upon large scale ungrounded dialogues and unstructured knowledge base. |
| Outcome: | The proposed framework outperforms state-of-the-art methods even in zero-resource setting. |
AmbigQA: Answering Ambiguous Open-domain Questions (2020.emnlp-main)
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| Challenge: | Existing open-domain question answering systems assume questions have a single welldefined answer. |
| Approach: | They propose an open-domain question answering task which involves finding every plausible answer and rewriting the question for each one to resolve the ambiguity. |
| Outcome: | The proposed task is based on a dataset covering 14,042 open-domain questions . it shows that strong models benefit from weakly supervised learning . |
META: Metadata-Empowered Weak Supervision for Text Classification (2020.emnlp-main)
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| Challenge: | Existing methods for weakly supervised text classification use text data alone to generate pseudo-labels . strong label indicators exist in metadata and it has been long overlooked due to challenges . |
| Approach: | They propose a framework that leverages metadata as an additional source of weak supervision by combining text data and metadata into a text-rich network. |
| Outcome: | The proposed framework exploits metadata as an additional source of weak supervision. |
KnowMAN: Weakly Supervised Multinomial Adversarial Networks (2021.emnlp-main)
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| Challenge: | Existing approaches to weakly supervised training lack labeled data . weakly-supervised training can result in heuristic but noisy labels . |
| Approach: | They propose a scheme that allows to control influence of signals associated with specific labeling functions. |
| Outcome: | The proposed scheme improves results compared to weakly supervised learning with a pre-trained transformer language model and a feature-based baseline. |
Weaker Than You Think: A Critical Look at Weakly Supervised Learning (2023.acl-long)
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| Challenge: | Weakly supervised learning is a popular approach for training machine learning models in low-resource settings. |
| Approach: | They propose to use weakly supervised learning to train models with noisy labels from weak sources instead of collecting expensive human annotations. |
| Outcome: | The proposed methods outperform weakly supervised methods on various NLP datasets and tasks on the test sets. |
Can VLMs Predict Future States? Bootstrapping World Models from Inverse Dynamics (2026.findings-acl)
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| Challenge: | unified vision–language models (VLMs) struggle to generate physically plausible transitions between frames from instructions. |
| Approach: | They find that VLMs struggle to generate physically plausible transitions between frames from instructions. |
| Outcome: | The proposed model outperforms state-of-the-art image editing models on Aurora-Bench . it achieves the best average human evaluation across all subsets of Aurora-bench compared with other models . |