Visual　question　answering　(VQA)　models　often　face　two　language　bias　challenges.　First,　they　tend　to　rely　solely　on　the　question　to　predict　the　answer,　often　overlooking　relevant　information　in　the　accompanying　images.　Second,　even　when　considering　the　question,　they　may　focus　only　on　the　wh-words,　neglecting　other　crucial　keywords　that　could　enhance　interpretability　and　the　question　sensitivity.　Existing　debiasing　methods　attempt　to　address　this　by　training　a　bias　model　using　question-only　inputs　to　enhance　the　robustness　of　the　target　VQA　model.　However,　this　approach　may　not　fully　capture　the　language　bias　present.　In　this　article,　we　propose　a　multimodality　counterfactual　dual-bias　model　to　mitigate　the　linguistic　bias　issue　in　target　VQA　models.　Our　approach　involves　designing　a　shared-parameterized　dual-bias　model　that　incorporates　both　visual　and　question　counterfactual　samples　as　inputs.　By　doing　so,　we　aim　to　fully　model　language　biases,　with　visual　and　question　counterfactual　samples,　respectively,　emphasizing　important　objects　and　keywords　to　relevant　the　answers.　To　ensure　that　our　dual-bias　model　behaves　similarly　to　an　ordinary　model,　we　freeze　the　parameters　of　the　target　VQA　model,　meanwhile　using　the　cross-entropy　and　Kullback-Leibler　(KL)　divergence　as　the　loss　function　to　train　the　dual-bias　model.　Subsequently,　to　mitigate　language　bias　in　the　target　VQA　model,　we　freeze　the　parameters　of　the　dual-bias　model　to　generate　pseudo-labels　and　then　incorporate　a　margin　loss　to　re-train　the　target　VQA　model.　Experimental　results　on　the　VQA-CP　datasets　demonstrate　the　superior　effectiveness　of　our　proposed　counterfactual　dual-bias　model.　Additionally,　we　conduct　an　analysis　of　the　unsatisfactory　performance　on　the　VQA　v2　dataset.　The　origin　code　of　the　proposed　model　is　available　at　https://github.com/Arrow2022jv/MCD