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Creators/Authors contains: "Zheng, Tang"

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  1. ; (, Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL))
    We introduce a method that self trains (or bootstraps) neural relation and explanation classifiers. Our work expands the supervised approach of (Tang and Surdeanu, 2022), 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 mod- els’ 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 Sainz et al. (2021) (without requiring an additional natural language inference component). 
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