| 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. |
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Denoising Multi-Source Weak Supervision for Neural Text Classification (2020.findings-emnlp)
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| Challenge: | Recent years have witnessed the rapid development of deep neural networks (DNNs) for text classification problems. |
| Approach: | They propose a label denoiser which estimates the source reliability using a conditional soft attention mechanism and reduces label noise by aggregating rule-annotated weak labels. |
| Outcome: | The proposed model outperforms state-of-the-art methods on sentiment, topic, and relation classifications and achieves comparable performance with fully-supervised methods even without labeled data. |
Named Entity Recognition through Deep Representation Learning and Weak Supervision (2021.findings-acl)
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| Challenge: | Weakly supervised named entity recognition (NER) uses noisy labels to estimate the true labels of a dataset. |
| Approach: | They propose a model to learn optimal assignments of latent NER tags using observed tokens and weak labels provided by labeling functions. |
| Outcome: | The proposed model improves the quality of weak labels on four public datasets. |
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. |
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. |
Weakly Supervised Attention Networks for Entity Recognition (D19-1)
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| Challenge: | Existing approaches to entity recognition require large amounts of token-level data, which can be expensive and cumbersome to obtain. |
| Approach: | They propose a weakly supervised model that can be annotated at word level from a corpus containing binary presence/absence labels. |
| Outcome: | The proposed model performs reasonably well on the task of entity recognition despite not having access to token-level ground truth data. |
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. |
AdvPicker: Effectively Leveraging Unlabeled Data via Adversarial Discriminator for Cross-Lingual NER (2021.acl-long)
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| Challenge: | Named entity recognition models rely on expensive labeled data for training, which is not always available across languages. |
| Approach: | They propose an adversarial approach where an encoder learns entity domain knowledge from labeled source-language data and better shared features are captured via adversarially trained discriminators. |
| Outcome: | The proposed approach outperforms existing state-of-the-art methods on standard benchmark datasets and outperformed existing methods on the target language. |
SepLL: Separating Latent Class Labels from Weak Supervision Noise (2022.findings-emnlp)
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| Challenge: | Existing methods for learning from weak labels use heuristics and heurism to create weak labels. |
| Approach: | They propose a weakly supervised learning paradigm that uses human intuitions to create weak (noisy) labels. |
| Outcome: | The proposed model is competitive with the state-of-the-art and yields a new best average performance. |
Beyond Black & White: Leveraging Annotator Disagreement via Soft-Label Multi-Task Learning (2021.naacl-main)
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| Challenge: | Prior work shows that disagreement between annotators can be useful in training models. |
| Approach: | They propose to use disagreements as an auxiliary task in a multi-task neural network to incorporate disagreements into models. |
| Outcome: | The proposed method significantly improves performance on NLP tasks beyond the standard approach and prior work. |
Learning from Noisy Labels for Entity-Centric Information Extraction (2021.emnlp-main)
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| Challenge: | Recent information extraction approaches can easily overfit noisy labels and suffer from performance degradation. |
| Approach: | They propose a co-regularization framework for entity-centric information extraction that optimizes neural models with task-specific losses and regularizes them to generate similar predictions based on agreement loss. |
| Outcome: | The proposed framework is optimized with task-specific losses and generates similar predictions based on agreement loss. |