Bootstrapping Neural Relation and Explanation Classifiers (2023)

Abstract: We introduce a method that self trains (or bootstraps) neural relation and explanation classifiers. Our work expands the supervised approach of CITATION, which jointly trains a relation classifier with an explanation classifier that identifies context words important for the relation at hand, to semi-supervised scenarios. In particular, our approach iteratively converts the explainable models’ outputs to rules and applies them to unlabeled text to produce new annotations. Our evaluation on the TACRED dataset shows that our method outperforms the rule-based model we started from by 15 F1 points, outperforms traditional self-training that relies just on the relation classifier by 5 F1 points, and performs comparatively with the prompt-based approach of CITATION (without requiring an additional natural language inference component).

## introduction recently @xcite proposed a supervised method that jointly trains a relation classifier (e.g., which extracts the relation per:city_of_birth between john and london in the sentence john was born in london) with an explanation classifier that identifies context words that are important for the relation at hand (e.g., born and in in the above example). one limitation of this method is that, similar to other neural approaches, it is data hungry. this is an important drawback for real-world applications where annotated data is expensive to obtain. in this work, we expand this approach to semisupervised scenarios where the only supervision comes from a few example rules. in particular, our method iteratively converts the explanations produced by the above method into rules, and uses these rules to generate new "silver" annotations that are added to the training data. the specific contributions of this effort are: ## related work for brevity, we focus our related work discussion on semi-supervised directions for information extraction that are closest to the proposed work: bootstrapping/self-training and recent prompt-based zero-or few-shot methods. ## approach similar to traditional bootstrapping @xcite , our approach iteratively trains its classifier with the currently annotated data and applies the resulting model to the raw data to produce new annotations. however, unlike traditional self training, which uses the classifier to annotate data, our approach converts the current model and data into rules, and uses the generated rules to annotate data. as we discuss in section 4 this performs better empirically. algorithm 1 shows the overall training procedure. we discuss the three key components below. ## conclusion we introduced a method that self trains (or bootstraps) a neuro-symbolic approach for relation extraction that combines neural relation and explanation classifiers. unlike traditional self-training, our approach uses rules for bootstrapping. in particular, our method iteratively converts the explainable models' outputs to rules and applies them to unlabeled text to produce new annotations. we evaluated our approach in a low-resource scenario where there is no labeled data, and the only supervision comes from a small number of seed patterns. our experiments showed that using rules in the bootstrapped training procedure is better than the typical self-training method that relies on neural model predictions. further, we show that we obtain similar performance with prompt-based models for relation extraction without the additional nli component required by such approaches.