Look at the First Sentence: Position Bias in Question Answering (2020)

Abstract: Many extractive question answering models are trained to predict start and end positions of answers. The choice of predicting answers as positions is mainly due to its simplicity and effectiveness. In this study, we hypothesize that when the distribution of the answer positions is highly skewed in the training set (e.g., answers lie only in the k-th sentence of each passage), QA models predicting answers as positions can learn spurious positional cues and fail to give answers in different positions. We first illustrate this position bias in popular extractive QA models such as BiDAF and BERT and thoroughly examine how position bias propagates through each layer of BERT. To safely deliver position information without position bias, we train models with various de-biasing methods including entropy regularization and bias ensembling. Among them, we found that using the prior distribution of answer positions as a bias model is very effective at reducing position bias, recovering the performance of BERT from 37.48% to 81.64% when trained on a biased SQuAD dataset.

## introduction question answering (qa) is a task of answering questions given a passage. large-scale qa datasets have attracted many researchers to build effective qa models, and with the advent of deep learning, recent qa models are known to outperform humans in some datasets @xcite @xcite . extractive qa is the task that assumes that answers always lie in the passage. based on this task assumption, various qa models are trained to predict the start and end positions as the answers. following the structure of earlier deep learning-based qa models @xcite @xcite , recent qa models provide positions of answers without much consideration @xcite @xcite . the popularity of predicting the answer positions is credited to the fact that it reduces the prediction space to o(n) where n is the length of an input document. it is more efficient and effective than directly generating answers from a large vocabulary space. furthermore, it reduces the qa task to a classification task which is convenient to model. nevertheless, very few studies have discussed the side effects of predicting the answer positions. could there be any unwanted biases when using answer positions as prediction targets? in this paper, we demonstrate that the models predicting the position can be severely biased when trained on datasets that have a very skewed answer position distribution. we define this as position bias as shown in figure 1 . models trained on a biased dataset where answers always lie in the same to examine the cause of the problem, we thoroughly analyze the learning process of qa models trained on the biased training sets, especially focusing on bert. our analysis shows that hidden representations of bert preserve a different amount of word information according to the word position when trained on the biased training set. the predictions of biased models also become more dependent on the first few words when the training set has answers only in the first sentences. to tackle the problem, we test various options, ranging from relative position encodings @xcite to ensemble-based de-biasing methods @xcite . while simple baselines motivated by our analysis improve the test performance, our ensemble-based de-biasing method largely improves the performance of most models. specifically, we use the prior distribution of answer positions as an additional bias model and train models to learn reasoning ability beyond the positional cues. contributions of our paper are in threefold; first, we define position bias in extractive question answering and illustrate that common extractive qa models suffer from it. second, we examine the reason for the failure of the biased models and show that positions can act as spurious biases. third, we show that the prior distribution of answer positions helps us to build positionally de-biased models, recovering the performance of bert from 37.48% to 81.64%. we also generalize our findings in many different positions and datasets. foot_0 2 https://github.com/dmis-lab/position-bias ## analysis we first demonstrate the presence of position bias using biased training sets sampled from squad @xcite and visualize how position bias propagates in bert. ## method to prevent models from learning a direct correlation between word positions and answers, we introduce simple remedies for bert and a bias ## experiments we first experiment the effects of various debiasing methods on three different qa models using both biased and full training sets. our next experiments generalize our findings in different sentence positions and different datasets such as newsqa @xcite and naturalquestions @xcite . ## related work various question answering datasets have been introduced with diverse challenges including reasoning over multiple sentences @xcite , answering multi-hop questions @xcite , and more @xcite @xcite . introduction of these datasets rapidly progressed the development of effective qa models @xcite @xcite @xcite @xcite , but most models predict the answer as positions without much discussion on it. our work builds on the analyses of dataset biases in machine learning models and ways to tackle them. for instance, sentence classification models in natural language inference and argument reasoning comprehension suffer from word statistics bias @xcite @xcite @xcite . on visual question answering, models often ignore visual information due to the language prior bias @xcite @xcite @xcite . several studies in qa also found that qa models do not leverages the full information in the given passage @xcite @xcite . adversarial datasets have been also proposed to deal with this type of problem @xcite . in this study, we define position bias coming from the prediction structure of qa models and show that positionally biased models can ignore information in different positions. our proposed methods are based on the bias ensemble method @xcite . ensembling with the bias model encourages the model to solve tasks without converging to bias shortcuts. @xcite conducted de-biasing experiments on various tasks including two qa tasks while they use tf-idf and the named entities as the bias models. it is worth noting that several models incorporate the pointer network to predict the answer positions in qa @xcite @xcite . also, instead of predicting positions, some models predict the n-grams as answers @xcite , generate answers in a vocabulary space @xcite , or use a generative model @xcite . we expect that these approaches suffer less from position bias and leave the evaluation of position bias in these models as our future work. ## conclusion most qa studies frequently utilize start and end positions of answers as training targets without much considerations. our study shows that most qa models fail to generalize over different positions when trained on datasets having answers in a specific position. our findings show that position can work as a spurious bias and alert researchers when building qa models and datasets. we introduce several de-biasing methods to make models to ignore the spurious positional cues, and find out that the sentence-level answer prior is very useful. our findings also generalize to different positions and different datasets. one limitation of our approach is that our method and analysis are based on a single paragraph setting which should be extended to a multiple paragraph setting to be more practically useful.