  While relation extraction is an essential task in knowledge acquisition and
representation, and new-generated relations are common in the real world, less
effort is made to predict unseen relations that cannot be observed at the
training stage. In this paper, we formulate the zero-shot relation extraction
problem by incorporating the text description of seen and unseen relations. We
propose a novel multi-task learning model, zero-shot BERT (ZS-BERT), to
directly predict unseen relations without hand-crafted attribute labeling and
multiple pairwise classifications. Given training instances consisting of input
sentences and the descriptions of their relations, ZS-BERT learns two functions
that project sentences and relation descriptions into an embedding space by
jointly minimizing the distances between them and classifying seen relations.
By generating the embeddings of unseen relations and new-coming sentences based
on such two functions, we use nearest neighbor search to obtain the prediction
of unseen relations. Experiments conducted on two well-known datasets exhibit
that ZS-BERT can outperform existing methods by at least 13.54\% improvement on
F1 score.

Computation and Language;Information Retrieval;Machine Learning