A　structured　semantic　knowledge　base　called　a　temporal　knowledge　graph　contains　several　quadruple　facts　that　change　throughout　time.　To　infer　missing　facts　is　one　of　the　main　challenges　with　temporal　knowledge　graph,　i.e.,　temporal　knowledge　graph　completion　(TKGC).　Transformer　has　strong　modeling　abilities　across　a　variety　of　domains　since　its　self-attention　mechanism　makes　it　possible　to　model　the　global　dependencies　of　input　sequences,　while　few　studies　explore　Transformer　encoders　for　TKGC　tasks.　To　address　this　problem,　we　propose　a　novel　end-to-end　TKGC　model　named　Transbe-TuckERTT　that　adopts　an　encoder-decoder　architecture.　Specifically,　t　he　proposed　model　employs　the　Transformer-based　encoder　to　facilitate　interaction　between　entities,　relations,　and　temporal　information　within　the　quadruple　to　generate　highly　expressive　embeddings.　The　TuckERTT　decoder　uses　encoded　embeddings　to　predict　missing　facts　in　the　knowledge　graph.　Experimental　results　demonstrate　that　our　proposed　model　outperforms　several　state-of-the-art　TKGC　methods　on　three　public　benchmark　datasets,　verifying　the　effectiveness　of　the　self-attention　mechanism　in　the　Transformer-based　encoder　for　capturing　dependencies　in　the　temporal　knowledge　graph.　　©　2023　IEEE.