Description Boosting for Zero-Shot Entity and Relation Classification

TL;DR

This paper introduces a description boosting approach for zero-shot entity and relation classification, improving model robustness and achieving state-of-the-art results by enhancing textual descriptions used for inference.

Contribution

It proposes a novel method for generating and ranking description variations, along with an ensemble technique to boost zero-shot classification performance.

Findings

Outperforms existing methods on four datasets

Achieves new state-of-the-art results in ZSL settings

Demonstrates robustness to description variations

Abstract

Zero-shot entity and relation classification models leverage available external information of unseen classes -- e.g., textual descriptions -- to annotate input text data. Thanks to the minimum data requirement, Zero-Shot Learning (ZSL) methods have high value in practice, especially in applications where labeled data is scarce. Even though recent research in ZSL has demonstrated significant results, our analysis reveals that those methods are sensitive to provided textual descriptions of entities (or relations). Even a minor modification of descriptions can lead to a change in the decision boundary between entity (or relation) classes. In this paper, we formally define the problem of identifying effective descriptions for zero shot inference. We propose a strategy for generating variations of an initial description, a heuristic for ranking them and an ensemble method capable of boosting the predictions of zero-shot models through description enhancement. Empirical results on four different entity and relation classification datasets show that our proposed method outperform existing approaches and achieve new SOTA results on these datasets under the ZSL settings. The source code of the proposed solutions and the evaluation framework are open-sourced.