  Predicting unseen relations that cannot be observed during the training phase
is a challenging task in relation extraction. Previous works have made progress
by matching the semantics between input instances and label descriptions.
However, fine-grained matching often requires laborious manual annotation, and
rich interactions between instances and label descriptions come with
significant computational overhead. In this work, we propose an efficient
multi-grained matching approach that uses virtual entity matching to reduce
manual annotation cost, and fuses coarse-grained recall and fine-grained
classification for rich interactions with guaranteed inference speed.
Experimental results show that our approach outperforms the previous State Of
The Art (SOTA) methods, and achieves a balance between inference efficiency and
prediction accuracy in zero-shot relation extraction tasks. Our code is
available at https://github.com/longls777/EMMA.
