Less Is Better: Recovering Intended-Feature Subspace to Robustify NLU Models (2022)

Abstract: Datasets with significant proportions of bias present threats for training a trustworthy model on NLU tasks. Despite yielding great progress, current debiasing methods impose excessive reliance on the knowledge of bias attributes. Definition of the attributes, however, is elusive and varies across different datasets. In addition, leveraging these attributes at input level to bias mitigation may leave a gap between intrinsic properties and the underlying decision rule. To narrow down this gap and liberate the supervision on bias, we suggest extending bias mitigation into feature space. Therefore, a novel model, Recovering Intended-Feature Subspace with Knowledge-Free (RISK) is developed. Assuming that shortcut features caused by various biases are unintended for prediction, RISK views them as redundant features. When delving into a lower manifold to remove redundancies, RISK reveals that an extremely low-dimensional subspace with intended features can robustly represent the highly biased dataset. Empirical results demonstrate our model can consistently improve model generalization to out-of-distribution set, and achieves a new state-of-the-art performance.

## introduction pretrained language models have achieved remarkable performance on a wide range of natural language understanding (nlu) benchmarks @xcite . however, when encountering more challenging test sets, they dramatically fail @xcite . studies indicate such a dilemma is mainly rooted in the model's reliance on specific dataset biases @xcite @xcite , which correlate well with labels but not for the intended underlying task. for instance, on the natural language inference (nli) task, models tend to use negation cues ("not", "no", etc.), for a contradiction prediction, whereas a learner intended to learn the underlying correlation based on the context semantics. to train a nlu model that captures the underlying correlation from biased datasets, current approaches focus on how to leverage kinds of supervision effectively. one of the most popular forms of such supervision is to explicitly construct a bias-only model under human annotations, e.g., a hypothesis-only model for nli task, and factor it out from the main model through ensemblebased training @xcite . another empirical line of research shifts supervision from bias type annotations to weak model learners. they find models with limited capacity @xcite or training on limited dataset @xcite prone to extract shortcut patterns first, the observation of which can be utilized to mitigate dataset bias. despite the supervision on bias has shown effectiveness in bias mitigation, the fundamental questions remain unsolved. on one hand, acquirement of supervision on the bias either from human knowledge or model learning behaviours, is often a laborious and expensive cost. moreover, considering the definition of bias attributes is elusive and varies across datasets, the external knowledge can not cover all types of biases in the dataset, leaving potential bias underexplored, e.g., bias beyond the definition or bias harder to learn. on the other hand, capturing bias only at the input examples is just like a black box, being oblivious to the intrinsic properties that drives model to make prediction. the toy example shown in figure 1 reveals that predefined bias does not necessarily lead the model to learn the unintended decision rule (i.e., constituent bias triggers an entailment prediction, negation bias triggers an contradiction prediction). hence, current debiasing methods inevitably fall short in above two limitations. on account of the consensus that shortcut features induced by biased examples are detrimental for prediction, various kinds of biases can thus be equivalently viewed as redundancies. when delving into feature space, closer to the decision rule to remove these redundant features, supervision of the attributes from biased examples can be liberated as well. therefore, we develop a novel model, recovering intended-feature subspace with knowledge-free (risk). aimed with purifying redundancies from feature space, risk reveals that for a highly biased dataset, a small subset of informative and shared features, i.e. intended ones, can give rise to a robust prediction. concretely, risk maps features into a lower manifold and learns an orthogonal projector spanned by geometric median subspace to recover the intended-feature subspace in an end-to-end manner. experimental results on three nlu tasks show risk outperforms other methods by a large margin, indicating its potential to mitigate bias and the prerequisites of supervision on biased attributes can be liberated. moreover, when transferring to more challenging out-of-distribution set, risk can consistently improve the robustness of nlu models. to sum up, our contributions are three-fold as follows: ## risk: feature-based debiasing without supervision on bias guided by the theoretical subspace modeling discussed above, in this section, we illustrate the detailed implementation of risk. in practice, we adopt autoencoder as the main architecture. leveraging autoencoder to map features into a lower manifold is the first stage of removing redundant features. we further add a simple but effective recovery layer within autoencoder to learn a orthogonal projection p, leaving the shared informative features to perform final predictions. ## experiments in this section, we provide comprehensive analysis on risk through extensive experiments on three nlu tasks, and compare out-of-distribution as well as in-distribution accuracy of risk with other debiasing methods to demonstrate its strength. ## analysis and discussion in this section, we construct supplementary experiments to further analyze risk's effectiveness. free of supervision on bias, we reveal that risk can deal with more challenging scenarios. ## related work we categorize the multiple lines of research devoted to mitigating dataset bias into three paradigms, in accordance with how the supervision is applied for bias mitigation. ## conclusion in this work, we shed light into feature subspace with the aim to create an underlying pathwayfrom the biased input examples to robust output prediction. viewing shortcut features as redundancy, we construct a simple but effective recovery layer within the autoencoder structure for bias mitigation. extensive experiments demonstrate the strengths of our model: better generalization, dataset-agnostic transferability and the robustness to more challenging scenarios. we believe this feature-based debiasing framework opens up new directions for establishing a trustworthy nlu model. meanwhile, our concise motivation and implementation throw out a thought-provoking question, that is for model, for feature, sometimes less can be better.