Pareto Probing: Trading Off Accuracy for Complexity

Abstract: The question of how to probe contextual word representations for linguistic structure in a way that is both principled and useful has seen significant attention recently in the NLP literature. In our contribution to this discussion, we argue for a probe metric that reflects the fundamental trade-off between probe complexity and performance: the Pareto hypervolume. To measure complexity, we present a number of parametric and non-parametric metrics. Our experiments using Pareto hypervolume as an evaluation metric show that probes often do not conform to our expectations -- e.g., why should the non-contextual fastText representations encode more morpho-syntactic information than the contextual BERT representations? These results suggest that common, simplistic probing tasks, such as part-of-speech labeling and dependency arc labeling, are inadequate to evaluate the linguistic structure encoded in contextual word representations. This leads us to propose full dependency parsing as a probing task. In support of our suggestion that harder probing tasks are necessary, our experiments with dependency parsing reveal a wide gap in syntactic knowledge between contextual and non-contextual representations.

• The paper introduces a Pareto-based framework that balances model complexity and accuracy in probing neural network embeddings.
• It employs both parametric and non-parametric complexity metrics, demonstrating that challenging tasks like dependency parsing better capture syntactic information.
• Empirical results show that contextual embeddings outperform simpler models on complex linguistic tasks, urging the need for refined probing methodologies.

"Pareto Probing: Trading Off Accuracy for Complexity" (2010.02180)

Introduction

The paper "Pareto Probing: Trading Off Accuracy for Complexity" addresses the ongoing challenge in NLP of effectively probing neural network representations to evaluate their encoded linguistic knowledge. It introduces a novel methodology that considers the trade-off between probe complexity and performance using the Pareto hypervolume as a metric. The authors advocate for utilizing dependency parsing over simpler probing tasks, like part-of-speech labeling, to reveal the syntactic richness of contextual embeddings like BERT.

Performance and Complexity Trade-Off

Probing is treated as a bi-objective optimization problem where the objectives are to minimize complexity while maximizing accuracy on linguistic tasks. The authors highlight that simplistic probing tasks may not accurately reflect the linguistic structure encoded, as shown in results where non-contextual embeddings, such as fastText, outperform BERT on easier tasks. This trade-off necessitates a balanced evaluation considering both dimensions using a Pareto frontier.

Probing with Pareto Frontier

The concept of Pareto optimality serves as a framework to analyze probes that balance simplicity and performance. The set of Pareto optimal probes creates a frontier that allows for comprehensive comparison across different models by examining those that are undominated in terms of both dimensions. The Pareto hypervolume, which bounds complexity, facilitates this evaluation and aids in identifying models that encode linguistic structures more effectively.

Parametric and Non-Parametric Complexity Metrics

The study introduces two complexity metrics: parametric, involving constraints like nuclear norms and rank within linear probes, and non-parametric, focused on a model's ability to memorize training data. These measures help quantify how complexity within a given probing architecture influences performance and provide insights into how well simple versus complex models capture linguistic information.

Empirical Results and Insights

Experiments conducted over diverse languages using representations from BERT, ALBERT, and RoBERTa show that contextual embeddings encode significant syntactic information, particularly in more complex tasks such as dependency parsing. Results demonstrate the limitations of toy tasks like POSL and DAL in distinguishing between the nuanced syntactic capabilities of different embeddings. The authors substantiate claims by showing how these embeddings yield better performance on more challenging parsing tasks, supporting the argument for more complex probing tasks.

Proposal for Harder Probing Tasks

The paper argues for the necessity of moving probing benchmarks to tasks that require modeling complete sentence structures, such as dependency parsing. The findings show that contextual models unveil their syntactic superiority through these challenging tasks, aligning with their known capacity to boost performance in comprehensive NLP applications.

Conclusion

"Pareto Probing: Trading Off Accuracy for Complexity" posits that analyzing neural network representations requires probing tasks that reflect real-world complexity. The Pareto-based approach coupled with dependency parsing tasks provides a clearer picture of the linguistic knowledge embedded in modern NLP models. This work calls for an advancement in probing methodologies tailored to harness the full potential of contextual embeddings, fostering deeper insights and more reliable evaluations.

The implementation insights and results from this research assist in framing probing as a nuanced evaluation metric, prompting further methodological refinements and advancements in understanding network representations.