Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual (1908.10763v2)

Abstract: Statistical natural language inference (NLI) models are susceptible to learning dataset bias: superficial cues that happen to associate with the label on a particular dataset, but are not useful in general, e.g., negation words indicate contradiction. As exposed by several recent challenge datasets, these models perform poorly when such association is absent, e.g., predicting that "I love dogs" contradicts "I don't love cats". Our goal is to design learning algorithms that guard against known dataset bias. We formalize the concept of dataset bias under the framework of distribution shift and present a simple debiasing algorithm based on residual fitting, which we call DRiFt. We first learn a biased model that only uses features that are known to relate to dataset bias. Then, we train a debiased model that fits to the residual of the biased model, focusing on examples that cannot be predicted well by biased features only. We use DRiFt to train three high-performing NLI models on two benchmark datasets, SNLI and MNLI. Our debiased models achieve significant gains over baseline models on two challenge test sets, while maintaining reasonable performance on the original test sets.

Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual

The paper "Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual" presents an innovative approach to addressing dataset bias in natural language inference (NLI). The work is a pivotal contribution to overcoming challenges posed by biases inherent in NLI datasets, which can severely impact the generalization ability of models beyond their training datasets.

Problem Statement

The paper focuses on biases that arise from superficial cues in NLI datasets, such as the presence of negation terms, which can mislead models into predicting contradictions where none exist. These biases result in models performing poorly in scenarios where the typical dataset cues are absent, as demonstrated by diverse challenge datasets.

Proposed Solution

The authors introduce a novel debiasing algorithm, referred to as \ours, which leverages residual fitting techniques to counteract the effects of known dataset biases. The method involves two primary steps:

1. Biased Model Development: Creating a biased model that exclusively utilizes features identified with dataset bias.
2. Debiased Model Training: Developing a debiased model trained on the residuals of the biased model, specifically targeting cases that cannot be resolved using biased features alone.

Experimental Validation

The efficacy of the \ours algorithm is substantiated through experiments carried out on three high-performing NLI models trained using two benchmark datasets, SNLI and MNLI. The debiased models demonstrate significant performance improvements on challenge test sets compared to baseline models, while maintaining satisfactory results on the original test sets. This highlights the robustness of the proposed method in handling distribution shifts.

Implications and Future Directions

This research provides substantial developments for natural language processing, especially in enhancing the generalization capability of models by systematically unlearning biases. It opens avenues for further investigation into algorithmic adaptations for other NLP tasks with similar challenges. The methodology may inspire future debiasing techniques in artificial intelligence, aiming to create models that are less reliant on dataset-specific heuristics and more adept at handling diverse real-world linguistic constructs.

Future work may explore refining the residual fitting technique or explore alternative ways of modeling biases to further improve NLI systems' robustness across varied applications potentially influencing advancements in broader AI fields like automatic dialogue systems and machine translation.