Towards Stable Natural Language Understanding via Information Entropy Guided Debiasing (2023)

Abstract: Although achieving promising performance, current Natural Language Understanding models tend to utilize dataset biases instead of learning the intended task, which always leads to performance degradation on out-of-distribution (OOD) samples. Toincrease the performance stability, previous debiasing methods empirically capture bias features from data to prevent the model from corresponding biases. However, our analyses show that the empirical debiasing methods may fail to capture part of the potential dataset biases and mistake semantic information of input text as biases, which limits the effectiveness of debiasing. To address these issues, we propose a debiasing framework IEGDB that comprehensively detects the dataset biases to induce a set of biased features, and then purifies the biased features with the guidance of information entropy. Experimental results show that IEGDB can consistently improve the stability of performance on OOD datasets for a set of widely adopted NLU models.

## introduction the natural language understanding (nlu) task requires a model to understand the semantics of input text and then infer the target label. state-ofthe-art nlu models such as bert have achieved impressive performance on various nlu tasks @xcite . however, recent analyses have demonstrated that these models may exploit the dataset biases, i.e., superficial surface cues that are spuriously associated with the target labels for making inferences @xcite @xcite . this leads to performance degradation on out-of-distribution (ood) challenge sets that are designed for making models relying on spurious associations obtaining incorrect predictions @xcite @xcite . ## background and preliminary analysis previous analyses demonstrate that nlu models may utilize dataset biases, leading to performance degradation on the ood datasets @xcite . hence, debiasing methods are proposed to increase the performance stability by detecting the dataset biases, and then regularizing the nlu model to enforce it focusing more on the semantics of input text. formally, given an instance (x i , y i ) where x i is the input text and y i is the target label, the debiasing methods aim at extracting a set of features h b @xmath0, which characterize the dataset biases within x i . then h b i can be employed to regularize an nlu model m nlu for preventing m nlu captures the dataset biases. early debiasing methods extract biased features based on human priors. however, the dataset biases could range from simple lexical overlap to complex language stylistic patterns @xcite @xcite . hence, manually designing biased features can be rather time-consuming. to address this issue, recent debiasing methods propose to train a biased model m b for automatically inducing a set of biased features previous automatic debiasing methods construct biased models by training an nlu model such as bert upon a tiny subset of the original training set @xcite , or a weak learner optimized upon the whole training set @xcite . essentially, these methods are constructed based on two main empirical assumptions about the inductive bias of models: (1) by restricting the available information for the biased feature induction model, it would have to overfit the dataset and capture the ungeneralizable dataset biases; however, the validity of these empirical assumptions does not have a theoretical guarantee. the overfitted models or weak models would also capture the semantic information. this leads to the impurity of the extracted biased features. furthermore, it leads to a dilemma: a model trained upon a tiny sub-training set or a weak learner can hardly learn to represent all the dataset biases. while if the amount of instances for training the model or the strength of the model is enhanced, the biased feature model would not focus on dataset biases only and would involve the semantic information. we conducted experiments to validate these arguments. the specific results are shown in sec 1 of the appendix. the incompleteness and impurity of biased features would affect the effectiveness of debiasing. hence we propose an information entropy guided automatic debiasing framework to compre-as figure 1 shows, the iegdb framework contains three parts: (1) a random biased feature induction forest to enrich the biased features; (2) information entropy guided biased features purification for excluding the semantic components within the extracted biased features; (3) then the main nlu model can be regularized using the identified biased features to increase the stability of performance. ## related work previous analysis demonstrates that the existence of dataset biases allows an nlu model to complete the task without learning the semantic information @xcite @xcite . this phenomenon exists in various different tasks, such as reading comprehension @xcite ), question answering @xcite , and fact verification @xcite . one line of debiasing methods mitigates the dataset biases based on prior knowledge @xcite ; @xcite ; @xcite . however, these methods are limited by the dependence on human prior. moreover, researches indicate that hidden biases may still remain after manually debiasing @xcite , highlighting the necessity of automatically and comprehensively detecting the dataset biases. to address these issues, automatic debiasing methods are proposed. @xcite automatically captures the dataset bias by training a shallow model on a tiny training set, while @xcite captures the dataset bias using a learner with limited capacity. however, these methods still rely on certain empirical assumptions that are not bounded to be valid, which affects the comprehensiveness and purity of the extracted biased features, and then limits the effectiveness of debiasing. in this paper, we propose an information entropy guided debiasing framework, which comprehensively and quantitatively extracts and purifies the biased features to further improve the stability of nlu models. ## conclusion in this paper, we propose an information entropy guided automatic debiasing nlu framework iegdb. by assembling multiple biased feature induction models, iegdb can induce biased features more comprehensively characterizing the dataset biases. then the extracted biased features are purified by identifying and excluding the semantic components within the biased features using information-guided blind source separation. furthermore, we provide a theoretical framework for quantitatively analyzing the comprehensiveness and purity of the extracted features. experimental results show that our approach can significantly increase the performance stability on ood samples for various nlu models, meanwhile keeping the in-distribution performance.