Can Question Generation Debias Question Answering Models? A Case Study on Question–Context Lexical Overlap (2021)

Abstract: Question answering (QA) models for reading comprehension have been demonstrated to exploit unintended dataset biases such as question–context lexical overlap. This hinders QA models from generalizing to under-represented samples such as questions with low lexical overlap. Question generation (QG), a method for augmenting QA datasets, can be a solution for such performance degradation if QG can properly debias QA datasets. However, we discover that recent neural QG models are biased towards generating questions with high lexical overlap, which can amplify the dataset bias. Moreover, our analysis reveals that data augmentation with these QG models frequently impairs the performance on questions with low lexical overlap, while improving that on questions with high lexical overlap. To address this problem, we use a synonym replacement-based approach to augment questions with low lexical overlap. We demonstrate that the proposed data augmentation approach is simple yet effective to mitigate the degradation problem with only 70k synthetic examples.

## introduction question answering (qa) for machine reading comprehension is a central task in natural language understanding, which requires a model to answer questions given textual contexts. pretrained language models have been successfully applied to qa and achieve scores higher than those of humans on benchmark datasets such as squad @xcite . however, qa models have been demonstrated to exploit unintended dataset biases instead of the intended solutions, and lack robustness to challenge test sets whose distributions are different from those of training sets @xcite @xcite , which could be a serious problem in real-world applications. question generation (qg) has also been extensively studied to augment qa datasets @xcite . it is demonstrated that qg can improve not only the in-domain generalization but also the out-of-distribution generalization capability of qa models @xcite @xcite . in other areas, data augmentation techniques have been successfully used to reduce dataset biases and increase the performance of machine learning models on under-represented samples in vision @xcite and language @xcite . thus, we assume that qg is useful to debias qa models and improve its robustness by augmenting qa datasets. however, it has not been fully studied whether existing qg models can contribute to debiasing qa models (i.e., improve the robustness of qa models to under-represented questions). in this study, we focus on question-context lexical overlap, inspired by the findings presented in @xcite . their work revealed that questions having low lexical overlap with context tend to require reasoning skills rather than superficial word matching, and existing qa models are not robust to these questions ( ## method we assume that if we augment questions with low qclo unlike existing neural qg approaches, the robustness of qa models to questions with low qclo can be improved. in this section, we describe the proposed method for generating questions with low qclo. we extend the idea of synonym replacement used in @xcite to reduce the lexical overlap. the proposed method is as follows: after repeating this procedure once for every ground-truth question in the training set, we obtain 70k synthetic questions with significantly lower lexical overlap, as indicated in figure 2 (ours). for example, what is heresy mainly at odds with? is converted into what is heterodoxy mainly at odds with?, and how many documents remain classified? is converted into how many text file remain classified?. because heterodoxy, text, and file do not appear in the contexts, the lexical overlap is reduced in each example. it is worth mentioning a couple of limitations of our method. first, synonym replacement may slightly change the meaning of questions depending on the context. second, our approach relies on the assumption that annotated questions are available, which makes it impossible to apply to unlabeled passages. ## experiments to determine the effect of data augmentation on improving the qa model robustness to questions with low qclo, we conducted experiments with several qg approaches. dataset we used the squad-du dataset as in §2. considering the qclo statistics of squad displayed in figure 2 , we split squad du dev and squad du test into easy and hard subsets that contain questions with qclo greater than 0.3, and the others, respectively. our easy and hard subsets offered concise, yet sufficient, evaluation in terms of qclo. baselines we adopted the following four baselines that use neural qg models for data augmentation. the distributions of the lexical overlap of these datasets are presented in figure 2 . we indicate that these methods are more biased towards high lexical overlap than squad du train , which was used as the training set for these qg models. experimental setups as in our previous experiment ( §2), we used bert-base and -large models, whose total number of parameters are 110m and 340m, respectively. dhingra et al. (2018) proposed to pretrain a qa model using synthetic data composed of cloze-style questions and then finetune it on the ground-truth data. we adopted the pretrain-and-fine-tune approach for the neural qg approaches, which generated over 1.2 million questions. however, as discussed by @xcite , we observed that when the size of the synthetic data was small or similar to the ground-truth data, a performance gain could not be obtained by the pretrain-and-fine-tune approach. thus, for the proposed approach, which generated 70k questions, we fine-tuned qa models on the ground-truth data randomly mixed with the generated data. we used the hugging face's implementation of bert @xcite . we use the adam (kingma and ba, 2014) optimizer with epsilon set to 1e-8. the batch size was 32 for all the settings. in both the pretraining and fine-tuning procedure, the learning rate decreased linearly from 3e-5 to zero. we train the qa models for one epoch for pretraining with synthetic data and two epochs for fine-tuning with squad du train . ## qualitative analysis to demonstrate the effect of the baseline qg models and proposed method qualitatively, we present examples in both the hard (@xmath0.3) and easy (@xmath1.3) subsets in the third example in in contrast, the fourth and fifth examples in in 2010 (ours), a number of workers committed suicide at a foxconn operations in china. apple, hp, and others stated that they were investigating the situation. foxconn guards have been videotaped beating employees. another employee killed himself in 2009 @xcite when an apple prototype went missing, and claimed in messages to friends, that he had been beaten and interrogated. despite being relatively unaffected by the embargo @xcite , the uk nonetheless faced an oil crisis of its own -a series of strikes by coal miners and railroad workers @xcite over the winter of 1973-74 became a major factor in the change of government. heath asked the british to heat only one room in their houses over the winter. the uk, germany, italy, switzerland and norway banned flying, driving and boating on sundays. sweden rationed gasoline and heating oil. the netherlands imposed prison sentences for those who used more than their ration of electricity. the robustness of qa models pretrained language models such as bert @xcite have surpassed the human score on the squad leaderboard. 7 however, such powerful qa models have been shown to exhibit the lack of robustness. a qa model that is trained on squad is not robust to paraphrased questions (gan and ng, 2019), implications derived from squad (ribeiro et al., 2019), questions with low lexical overlap @xcite , and other qa datasets @xcite @xcite . @xcite showed that extractive qa model can suffer from positional bias and fail to generalize to different answer positions. the lack of robustness demonstrated in these studies can be explained by shortcut learning of deep neural networks @xcite . a high score on an in-distribution test set can be achieved by just exploiting unintended dataset biases @xcite . therefore, evaluating qa models only on an in-distribution test set is not enough to evaluate the robustness of the qa models. question generation for question answering qg has been studied extensively in order to augment qa datasets and boost the qa performance, which has been evaluated primarily on squad @xcite @xcite ). question answer pair generation, which consists of answer candidate extraction and qg, has been also received attention because question-worthy answers for the input of qg are not freely available @xcite @xcite . the de facto standard of qg models is to utilize a copy mechanism @xcite . the tendency of qg models to copy words from textual contexts as indicated in figure 2 is partially due to this copy mechanism. while the existing qg works have increased the bleu scores on squad foot_7 and successfully generated fluent questions in terms of human scores, the bias regarding lexical overlap in qg has not received sufficient attention. data augmentation and dataset bias data augmentation has been widely used in other domains to reduce dataset biases such as the background bias in person re-identification @xcite , the gender bias in coreference resolution @xcite , and the lexical bias in natural language inference (zhou and bansal, 2020). these works repeated training examples or added synthetic data to increase under-represented samples and reduce the imbalance in a training set. our proposed approach has the same motivation as these works. on the other hand, data augmentation can unintentionally introduce or amplify dataset bias. backtranslation @xcite , which is the common data augmentation approach for machine translation, can introduce the translationese bias. that is, machine translation systems trained with back-translation, compared to ones without backtranslation, can enhance the bleu scores when the input is translationese (i.e., human-translated texts) but harm the bleu scores when the input is naturally occurring texts @xcite . this phenomenon is analogous to the observation in our work, where we demonstrated that squad qg models are biased towards generating questions with high qclo, and this tendency can harm the qa performance on questions with low qclo while improving that on questions with high qclo. ## conclusion we demonstrated that not only qa models but also qg models are biased in terms of the questioncontext lexical overlap. to determine the influence of the bias, we analyzed the qa performance with data augmentation using the recent qg models. we demonstrated that they frequently degraded the qa performance on questions with low lexical overlap, while improving that on questions with high lexical overlap when using bert-base. to address this problem, we designed a simple approach using synonym replacement to debias a qa dataset. we demonstrated that the proposed approach improved the qa performance on questions with low lexical overlap while maintaining or slightly degrading the overall scores with only 70k synthetic examples. our results suggest that future research in qg for data augmentation should exercise caution to prevent the amplification of dataset bias in terms of lexical overlap. in addition, what features are learned by data augmentation with neural qg models is worth to be explored in more detail to clarify what is improved and what is not improved by qg. it is also worth investigating whether our findings still hold in other qa datasets where annotated questions have lower lexical overlap than those in squad.