Zero-shot Relation Classification from Side Information
- URL: http://arxiv.org/abs/2011.07126v2
- Date: Thu, 18 Nov 2021 19:02:31 GMT
- Title: Zero-shot Relation Classification from Side Information
- Authors: Jiaying Gong and Hoda Eldardiry
- Abstract summary: A zero-shot learning approach mimics the way humans learn and recognize new concepts with no prior knowledge.
ZSLRC uses advanced networks that are modified to utilize weighted side (auxiliary) information.
ZSLRC significantly outperforms state-of-the-art methods on supervised learning, few-shot learning, and zero-shot learning tasks.
- Score: 5.609443065827996
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We propose a zero-shot learning relation classification (ZSLRC) framework
that improves on state-of-the-art by its ability to recognize novel relations
that were not present in training data. The zero-shot learning approach mimics
the way humans learn and recognize new concepts with no prior knowledge. To
achieve this, ZSLRC uses advanced prototypical networks that are modified to
utilize weighted side (auxiliary) information. ZSLRC's side information is
built from keywords, hypernyms of name entities, and labels and their synonyms.
ZSLRC also includes an automatic hypernym extraction framework that acquires
hypernyms of various name entities directly from the web. ZSLRC improves on
state-of-the-art few-shot learning relation classification methods that rely on
labeled training data and is therefore applicable more widely even in
real-world scenarios where some relations have no corresponding labeled
examples for training. We present results using extensive experiments on two
public datasets (NYT and FewRel) and show that ZSLRC significantly outperforms
state-of-the-art methods on supervised learning, few-shot learning, and
zero-shot learning tasks. Our experimental results also demonstrate the
effectiveness and robustness of our proposed model.
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