Visual dialog requires models to give reasonable answers accordingto a series of coherent questions and related visual concepts inimages. However, most current work either focuses on attentionbased fusion or pre-training on large-scale image-text pairs, ignoring the critical role of explicit vision-language alignment in visualdialog. To remedy this defect, we propose a novel unsupervisedand pseudo-supervised vision-language alignment approach forvisual dialog (AlignVD). Firstly, AlginVD utilizes the visual anddialog encoder to represent images and dialogs. Then, it explicitlyaligns visual concepts with textual semantics via unsupervised andpseudo-supervised vision-language alignment (UVLA and PVLA)Specifically, UVLA utilizes a graph autoencoder, while PVLA usesdialog-guided visual grounding to conduct alignment. Finally, basedon the aligned visual and textual representations, AlignVD givesa reasonable answer to the question via the cross-modal decoderExtensive experiments on two large-scale visual dialog datasetshave demonstrated the effectiveness of vision-language alignmentand our proposed AlignVD achieves new state-of-the-art results. Inaddition, our single model has won first place on the visual dialogchallenge leaderboard with a NDCG metric of 78.70, surpassing theprevious best ensemble model by about 1 point.