  Generalization beyond in-domain experience to out-of-distribution data is of
paramount significance in the AI domain. Of late, state-of-the-art Visual
Question Answering (VQA) models have shown impressive performance on in-domain
data, partially due to the language priors bias which, however, hinders the
generalization ability in practice. This paper attempts to provide new insights
into the influence of language modality on VQA performance from an empirical
study perspective. To achieve this, we conducted a series of experiments on six
models. The results of these experiments revealed that, 1) apart from prior
bias caused by question types, there is a notable influence of postfix-related
bias in inducing biases, and 2) training VQA models with word-sequence-related
variant questions demonstrated improved performance on the out-of-distribution
benchmark, and the LXMERT even achieved a 10-point gain without adopting any
debiasing methods. We delved into the underlying reasons behind these
experimental results and put forward some simple proposals to reduce the
models' dependency on language priors. The experimental results demonstrated
the effectiveness of our proposed method in improving performance on the
out-of-distribution benchmark, VQA-CPv2. We hope this study can inspire novel
insights for future research on designing bias-reduction approaches.
