Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases (1909.03683v1)

Abstract: State-of-the-art models often make use of superficial patterns in the data that do not generalize well to out-of-domain or adversarial settings. For example, textual entailment models often learn that particular key words imply entailment, irrespective of context, and visual question answering models learn to predict prototypical answers, without considering evidence in the image. In this paper, we show that if we have prior knowledge of such biases, we can train a model to be more robust to domain shift. Our method has two stages: we (1) train a naive model that makes predictions exclusively based on dataset biases, and (2) train a robust model as part of an ensemble with the naive one in order to encourage it to focus on other patterns in the data that are more likely to generalize. Experiments on five datasets with out-of-domain test sets show significantly improved robustness in all settings, including a 12 point gain on a changing priors visual question answering dataset and a 9 point gain on an adversarial question answering test set.

• The paper introduces a two-stage ensemble strategy that explicitly decouples bias exploitation from robust learning.
• Experiments show up to 12-point gains in VQA and 9-point improvements on adversarial QA, demonstrating significant performance boosts.
• The method outperforms traditional reweighting strategies and paves the way for developing domain-agnostic AI systems.

Ensemble-Based Methods for Addressing Dataset Biases in AI Models

The paper "Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases," authored by Christopher Clark, Mark Yatskar, and Luke Zettlemoyer, explores the fundamental challenge of dataset biases in AI models. Specifically, the paper addresses how state-of-the-art models often rely on superficial patterns in datasets that do not generalize well to out-of-domain or adversarial scenarios.

Problem Definition

A pervasive issue in machine learning, especially in tasks like textual entailment and visual question answering (VQA), is the model's tendency to learn from dataset biases. Such biases typically manifest as models learning to associate certain keywords with entailments or guessing likely answers without proper validation. These biases restrict model adaptability and robustness, especially when faced with data that deviates from training set distributions.

Proposed Methodology

The authors propose a two-stage ensemble-based methodology to mitigate the impact of dataset biases:

1. Naive Model Training: The first stage involves training a naive model that exclusively capitalizes on known dataset biases. This model is deliberately designed to perform well on biased training data yet falters under domain shifts.
2. Robust Model Training: In the second stage, a robust model is trained in conjunction with the naive model to ensure it focuses on non-biased data patterns. The ensemble approach encourages it to learn alternative strategies more likely to generalize.

This dual-stage approach effectively prevents the robust model from adopting biased strategies by leveraging the bias-only model as a baseline.

Empirical Evaluation

The paper validates its approach through experiments spanning five datasets, each presenting distinct domain shifts. Among the notable results, there is a 12-point gain achieved on a challenging VQA dataset and a 9-point improvement on an adversarial QA test set. The bias product ensemble method demonstrates consistent success across multiple tasks, while enhancements like the learned-mixin approach further improve outcomes.

Comparative Analysis

The research compares its methodology against traditional reweighting strategies and concludes that the ensemble methods generally outperform simpler baselines. The flexibility of the ensemble approach allows it to adapt dynamically, outperforming methods reliant on fixed weights or biases.

Theoretical and Practical Implications

From a theoretical perspective, the paper's method introduces a structured way to address and utilize known biases, reshaping how models are trained to focus on generalizable patterns. Practically, it suggests the possibility of creating more reliable and domain-agnostic AI systems capable of maintaining performance across diverse datasets. The ability to prevent models from leveraging superficial biases directly impacts model deployment in real-world applications where data variability is a norm.

Future Directions

Potential future work could focus on automating the detection of biases, which would enhance the applicability of these methods without explicit annotations. This would allow machine learning practitioners to apply robust training paradigms even in the absence of explicit bias identification. Moreover, expanding these methods to other AI domains beyond language and vision tasks could lead to broader insights into bias mitigation.

The paper contributes a robust methodological framework poised to improve AI's adaptability by methodically tackling the challenge of dataset biases, signaling a vital step toward the development of more resilient AI systems.