End-to-End Trainable Soft Retriever for Low-resource Relation Extraction

This study addresses a crucial challenge in instance-based relation extraction using text generation models: end-to-end training in target relation extraction task is not applicable to retrievers due to the non-differentiable nature of instance selection. We propose a novel End-to-end TRAinable Soft K-nearest neighbor retriever (ETRASK) by the neural prompting method that utilizes a soft, differentiable selection of the $k$ nearest instances. This approach enables the end-to-end training of retrievers in target tasks. On the TACRED benchmark dataset with a low-resource setting where the training data was reduced to 10\%, our method achieved a state-of-the-art F1 score of 71.5\%. Moreover, ETRASK consistently improved the baseline model by adding instances for all settings. These results highlight the efficacy of our approach in enhancing relation extraction performance, especially in resource-constrained environments. Our findings offer a promising direction for future research with extraction and the broader application of text generation in natural language processing.