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. |
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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. |
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. |
Learning Concept Abstractness Using Weak Supervision (D18-1)
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Ella Rabinovich, Benjamin Sznajder, Artem Spector, Ilya Shnayderman, Ranit Aharonov, David Konopnicki, Noam Slonim
| Challenge: | Existing methods for inferring abstractness of words and expressions without labeled data are limited and limited. |
| Approach: | They propose a weakly supervised approach for inferring the property of abstractness of words and expressions in the absence of labeled data. |
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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. |
Variational Sequential Labelers for Semi-Supervised Learning (D18-1)
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| Challenge: | a family of multitask variational methods for semi-supervised sequence labeling is currently unclear how to use them in the context of sequence labelling. |
| Approach: | They propose a family of multitask variational methods for semi-supervised sequence labeling using latent variables and a discriminative labeler. |
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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. |
ULF: Unsupervised Labeling Function Correction using Cross-Validation for Weak Supervision (2023.emnlp-main)
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| Challenge: | A cost-effective alternative to manual data labeling is weak supervision (WS), where data samples are automatically annotated using a predefined set of labeling functions (LFs). |
| Approach: | They propose an algorithm which denoises WS data by leveraging models trained on all but some LFs to identify and correct biases specific to the held-out LF. |
| Outcome: | The proposed algorithm denoises WS data by leveraging models trained on all but some LFs to identify and correct biases specific to the held-out LF. |
Weakly- and Semi-supervised Evidence Extraction (2020.findings-emnlp)
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| Challenge: | Existing methods to combine evidence annotations with document labels are limited to a minority of training examples. |
| Approach: | They propose to combine evidence annotations with abundant document labels for evidence extraction task. |
| Outcome: | The proposed method outperforms baselines on two classification tasks with evidence annotations. |
Noise-injected Consistency Training and Entropy-constrained Pseudo Labeling for Semi-supervised Extractive Summarization (2022.coling-1)
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| Challenge: | Existing studies on semi-supervised learning methods focus on how to effectively utilize abundant unlabeled data. |
| Approach: | They propose a semi-supervised consistency training method to regularize model predictions and a pseudo-labeling strategy to obtain high-confidence labels from unlabeled predictions. |
| Outcome: | The proposed method improves extractive summarization over an insufficient labeled dataset. |
Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with Noisy Labels (D19-1)
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| Challenge: | Existing approaches to improve supervised labeling with noisy training data do not take the input features into account or they need to learn the noise modeling from scratch. |
| Approach: | They propose to cluster training data using input features and compute different confusion matrices for each cluster. |
| Outcome: | The proposed model improves on low-resource named entity recognition settings in several languages, compared with other models which do not take the input features into account or need to learn noise modeling from scratch. |