Fusion Makes Perfection: An Efficient Multi-Grained Matching Approach for Zero-Shot Relation Extraction (2024)

Abstract: 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.

## introduction relation extraction (re) is an important task of natural language processing (nlp), which aims to identify the relation between a pair of entities within a given sentence. previous re models @xcite @xcite have impressive performance through large-scale supervised learning based on high-quality labeled data. however, collecting sufficient data for every new relation type is laborious in practice. this leads to the necessity of zero-shot re task, which involves extracting unobserved relations. recently, semantic matching @xcite has become a mainstream paradigm of zero-shot re, which matches a given input with a corresponding label description. promptmatch @xcite performed self-attention over ## experiments we conduct our experiments on the fewrel @xcite and wiki-zsl (chen and li, 2021) datasets. specific details about the datasets and experimental details are provided in appendix a. ## conclusions in this work, we propose a fusion method for ze-rore named emma, which enhances performance in the zerore task by combining coarse-grained recall and fine-grained classification, while maintaining efficient inference capabilities. experimental results demonstrate that our approach outperforms sota methods in matching f1 scores while maintaining rapid inference.