End-to-End Weak Supervision (2107.02233v3)

Abstract: Aggregating multiple sources of weak supervision (WS) can ease the data-labeling bottleneck prevalent in many machine learning applications, by replacing the tedious manual collection of ground truth labels. Current state of the art approaches that do not use any labeled training data, however, require two separate modeling steps: Learning a probabilistic latent variable model based on the WS sources -- making assumptions that rarely hold in practice -- followed by downstream model training. Importantly, the first step of modeling does not consider the performance of the downstream model. To address these caveats we propose an end-to-end approach for directly learning the downstream model by maximizing its agreement with probabilistic labels generated by reparameterizing previous probabilistic posteriors with a neural network. Our results show improved performance over prior work in terms of end model performance on downstream test sets, as well as in terms of improved robustness to dependencies among weak supervision sources.

• The paper introduces WeaSEL, a novel end-to-end framework that trains neural networks directly from multiple weak supervision sources.
• It employs sample-dependent source accuracies and a symmetric noise-aware loss to robustly align weak labels with target predictions.
• Empirical evaluations reveal that WeaSEL outperforms competitive methods by up to 6.1 F1 points across diverse datasets.

End-to-End Weak Supervision: A Detailed Examination

The paper End-to-End Weak Supervision introduces WeaSEL (Weakly Supervised End-to-End Learner), a framework designed to efficiently train a neural network directly from multiple sources of weak supervision. This research contributes to the ongoing effort to simplify and improve the machine learning pipeline by addressing the data-labeling bottleneck that plagues many supervised learning scenarios.

Problem Context and Proposal

Traditional supervised learning techniques demand substantial quantities of labeled data, an aspect often expensive and resource-intensive to fulfill, especially when involving domain experts. Meanwhile, weak supervision has emerged as a helpful bridge, allowing systems to harness noisy, heuristic-based, or loosely specified labels for tasks such as sentiment analysis or entity recognition. In the current paradigm, however, these resources are typically handled through a two-step methodology: 1) leveraging a probabilistic latent variable model without considering the ultimate performance of the downstream application, followed by 2) employing this model to develop the main classifier. Research remains complicated by computational challenges and the practical inadequacy of assumptions required for modeling, such as source independence and correct specification of dependencies.

WeaSEL discards this traditionally bifurcated approach. It proposes a streamlined, end-to-end neural network-based system that directly learns from weak supervision. This model is trained by maximizing the alignment of a network-encoded weak supervision aggregate with reference models' predictions. Notably, WeaSEL outperforms competing methodologies concerning model resilience to dependencies among weak supervision sources, suggesting a more generalized and robust application.

Key Features and Methodological Innovations

1. Sample-Dependent Source Accuracies: WeaSEL enhances flexibility by learning sample-specific labeling function accuracies. It incorporates features during this determination, thus addressing weaknesses in past approaches where such context was ignored.
2. Neural Encoder Architecture: The method introduces a neural network that, through information permeation between features and labeling function outputs, produces soft labels reparameterized from the probabilistic labels typically reliant on prior knowledge or assumptions.
3. Symmetric Loss Optimization: Packaging the neural encoder in an end-to-end fashion permits WeaSEL to optimize using a symmetric noise-aware loss function, leading to robust agreement-maximizing alignment across data predictions inherently different in source and construction.

Empirical Validation and Results

The authors executed a thorough empirical evaluation contrasting WeaSEL against existing approaches (e.g., Snorkel, Crowdsourcing frameworks), showcasing its superior performance across several datasets spanning sentiment analysis, professional classification tasks, and image aggregation. They highlight quantitative benefits, reporting improvements of up to 6.1 F1 points over the best-performing competitive models in exercises involving multiple benchmark datasets.

Theoretical and Practical Implications

WeaSEL posits an advantageous alternative, reframing how weak supervision aggregates are utilized, while enhancing robustness against adversarial information sources and correlation spikes. Such resilience suggests broad applicability, providing practical value across differing domains.

Theoretical implications point to the potential of neural networks to alleviate dependency assumptions that restrict traditional generative models. Furthermore, the method’s robust performance, irrespective of correlation dynamics or adversarial labeling functions, hints that future models can harness even noisier or loosely defined heuristic labels without descending performance spirals.

Concluding Thoughts and Future Directions

The WeaSEL framework constitutes a forward step in leveraging weak supervision more efficiently and accurately for machine learning tasks. Potential avenues for future work include exploring hybrid models incorporating various neural architectures, extending this foundation to encompass more complex data distributions, and further refining the encoder to enhance adaptability of class boundary detection amidst increased dataset heterogeneity. Additionally, investigating potential constraints from reliance on specific architectural parameters or inductive biases inherent in the encoder would elucidate other domains of applicability and performance enhancements.

The research underpins a compelling approach to machine learning where data remains sparse or noisy, providing nuanced perspectives for deploying AI across expansive real-world scenarios.