Improving QA Generalization by Concurrent Modeling of Multiple Biases (2020)

Abstract: Existing NLP datasets contain various biases that models can easily exploit to achieve high performances on the corresponding evaluation sets. However, focusing on dataset-specific biases limits their ability to learn more generalizable knowledge about the task from more general data patterns. In this paper, we investigate the impact of debiasing methods for improving generalization and propose a general framework for improving the performance on both in-domain and out-of-domain datasets by concurrent modeling of multiple biases in the training data. Our framework weights each example based on the biases it contains and the strength of those biases in the training data. It then uses these weights in the training objective so that the model relies less on examples with high bias weights. We extensively evaluate our framework on extractive question answering with training data from various domains with multiple biases of different strengths. We perform the evaluations in two different settings, in which the model is trained on a single domain or multiple domains simultaneously, and show its effectiveness in both settings compared to state-of-the-art debiasing methods.

## introduction as a result of annotation artifacts, existing nlp datasets contain shallow patterns that correlate with target labels @xcite @xcite @xcite . models tend to exploit these shallow patterns-which we refer to as biases in this paper-instead of learning general knowledge about solving the target task. existing debiasing approaches weaken the impact of such biases by disregarding or down1 the code and data are available at https://github.com/ukplab/ qa-generalization-concurrent-debiasing . importantly, the majority of existing debiasing approaches only deal with a single bias. they improve the performance scores on a targeted adversarial evaluation set, while typically resulting in performance decreases on the original datasets, or on adversarial datasets that contain different types of biases @xcite @xcite . in this paper, we show that modeling multiple biases is a key factor to benefit from debiasing methods for improving both in-domain performance and out-of-domain generalization, and propose a new debiasing framework for concurrent modeling of multiple biases during training. a key challenge for developing a general framework that can handle multiple biases is to properly combine them when various biases' strength is different in each dataset. previous work has found that if the ratio of biased examples is high, down-weighting, or disregarding all of them results in an insufficient training signal, which leads to performance decreases @xcite . therefore, we propose a novel multi-bias weighting function that weights each example according to multiple biases and based on each bias' strength in the training domain. we incorporate the multi-bias weights in the training objective by adjusting the loss according to the bias weights of individual training examples so that the model relies on more general patterns of the data. we evaluate our framework with extractive question answering (qa), for which a wide range of datasets from different domains exist-some contain crucial biases @xcite @xcite . existing approaches to improve generalization in qa either are only applicable when there exist multiple training domains @xcite @xcite or rely on models and ensembles with larger capacity @xcite @xcite . in contrast, our novel debiasing approach can be applied to both single and multi-domain scenarios, and it improves the model generalization without requiring larger pre-trained language models. we compare our framework with the two stateof-the-art debiasing methods of @xcite and @xcite . we study its impact in two different scenarios where the model is trained on a single domain, or multiple domains simultaneously. our results show the effectiveness of our framework compared to other debiasing methods, e.g., when the model is trained on a single domain, it improves generalization over six unseen datasets by around two points on average while the improvement is less than 0.5 points for other debiasing approaches. ## related work debiasing methods there is a growing amount of research literature on various debiasing methods to improve the robustness of models against individual biases in the training data @xcite @xcite @xcite . the central idea of the methods proposed in previous work is to reduce the impact of training examples that contain a bias. existing work either reduces the importance of biased examples in the loss function @xcite , lowers the confidence on biased examples (utama et al., 2020), or trains an ensemble of a bias model for learning biased examples, and a base model for learning from non-biased examples @xcite @xcite . a crucial limitation of the majority of existing methods is that they only target a single bias. while they improve the performances on the adversarial evaluation sets crafted for this particular bias, they lead to lower performance scores on non-targeted evaluation sets including the in-domain data @xcite , i.e., unlearning a specific bias does not indicate that the model has learned more general patterns of the data @xcite . we thus need debiasing approaches that help the model to learn from less-biased patterns of the data and improve its overall performance across various datasets that are not biased or may contain different biases. we compare our framework with recently proposed debiasing methods of @xcite and @xcite . utama et al. (2020) address a single bias. while improving the performance on the adversarial evaluation set, they also maintain the performance on the in-domain data distribution, which are exceptions to the aforementioned methods. mahabadi et al. (2020) handle multiple biases jointly and show that their debiasing methods can improve the performance across datasets if they fine-tune their debiasing methods on each target dataset to adjust the debiasing parameters. however, the impact of their method is unclear on generalization to unseen evaluation sets. in contrast to these state-of-the-art debiasing methods, we (1) concurrently model multiple biases without requiring any information about evaluation datasets, and (2) show that our debiasing framework achieves improvements in in-domain, as well as unseen out-of-domain datasets. generalization in qa the ability to generalize models to unseen domains is important across a variety of qa tasks @xcite @xcite . in this work, we focus on extractive qa. in this context, the mrqa workshop held a shared task dedicated to evaluating the generalization capabilities of qa models to unseen target datasets @xcite . the winning team @xcite uses an ensemble of multiple pre-trained language models, which includes xlnet @xcite and ernie @xcite . other submissions outperform the baseline by using more complex models with more parameters and better pre-training. for example, figure 1 : an illustration of our debiasing framework. the teacher and bias models are trained beforehand. during training, the corresponding teacher model for the input example outputs a prediction distribution, which will be used for distilling the knowledge to the student. each bias model generates a bias weight for the examples. we combine all the bias weights and use them to adapt the distillation loss. @xcite al. (2019 ) achieve considerable improvements by simply fine-tuning xlnet instead of bert, and longpre et al. (2019) achieve further improvements by augmenting the training data with additional unanswerable questions. the proposed methods by takahashi et al. (2019) and lee et al. (2019) for improving generalization leverage the fact that multiple training sets are available from different domains. for instance, takahashi et al. (2019) assign an expert to each in-domain dataset, and lee et al. (2019) introduce a domain discriminator to learn domain invariant features that are shared between datasets. their methods are thus not applicable to a single domain scenario. unlike the methods mentioned above, in this paper, we propose a model-agnostic approach to handle biases of the training data for improving the generalization capability of qa models. our proposed approach improves generalization without requiring any additional training data or employing larger models or ensembles. ## multi-bias debiasing framework let d @xmath0, . . . d tn } be the set of n training datasets, and d @xmath1, . . . d em } be the set of m evaluation sets that represent out-of-domain data. each example x i in both training and evaluation datasets contains a question q i , a context c i , and an answer span a i as the input. the corresponding output for x i is the start s i and end e i indices, which denote the span of the correct answer in c i . our goal is to train a single model on d t that achieves good zero-shot transfer performances on d e , i.e., obtaining a generalizable model that transfers well to unseen domains. to achieve this, we propose a novel debiasing framework that models multiple biases of the training data. the framework consists of four components (see figure 1 ): (1) multi-domain knowledge distillation (kd) to distill the knowledge from mul-tiple teachers into a single student model ( § 3.1); in the following, we will describe the four components in more detail. ## discussion and analysis in this section, we discuss the benefits and limitations of our framework.