Before revision:  In particular, (1) We split each triple into two parts, i.e. , i) head entity plus relation and ii) tail entity, process both contexts separately by a Transformer encoder, and combine the encoding outputs to derive the prediction. This Siamese-like architecture avoids the combinatorial explosion of candidate triples and significantly improves the efficiency, especially during inference; ( 2) We cover the contextualized semantics of the triples in the encoder so it can handle unseen entities during inference, which promisingly improves the generalization ability;
After revision:  In particular, (1) We split each triple into two parts, i.e. , and ( 2) We cover the contextualized semantics of the triples in the encoder so it can handle unseen entities during inference, which promisingly improves the generalization ability;