Challenge: Weak supervision and data programming are powerful tools to support information extraction models.
Approach: They propose a prototype-based method to denoise weakly supervised training data . they use a model to model the correct contexts for a given target value .
Outcome: The proposed method achieves 9% accuracy gain in attribute value extraction in e-commerce websites.

Similar Papers

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.
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.
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.
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.
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.
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.
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.
Weak Reward Model Transforms Generative Models into Robust Causal Event Extraction Systems (2024.emnlp-main)

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Challenge: Existing evaluation metrics that reflect the performance of causal event extraction tasks are poorly reflecting the inherent ambiguity of cause and effect boundaries.
Approach: They propose to use a weak-to-strong supervision method to train an evaluation model while still achieving high performance in training an RL model.
Outcome: The proposed method achieves high agreement with human-annotated data while still achieving high performance in training an RL model.
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.
Named Entity Recognition without Labelled Data: A Weak Supervision Approach (2020.acl-main)

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Challenge: Named Entity Recognition (NER) performance often degrades when applied to target domains that differ from the texts observed during training.
Approach: They propose a method to learn NER models in the absence of labelled data through weak supervision by using a broad spectrum of labelling functions to automatically annotate texts from the target domain.
Outcome: The proposed approach improves on two English datasets and shows that it improves by 7 percentage points on entity-level F1 scores compared to an out-of-domain neural NER model.

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