ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning
This addresses the challenge of predicting new, unseen relations in knowledge acquisition, which is common in real-world applications, representing an incremental advance over existing methods.
The paper tackles the problem of zero-shot relation extraction for unseen relations by proposing ZS-BERT, a multi-task learning model that uses relation descriptions to project sentences and relations into an embedding space, achieving at least a 13.54% improvement in F1 score on two datasets.
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.