A dependency-parsing method and a LEGAL-BERT-based classifier both reach roughly 80-84% accuracy for identifying regulatory statements in EU law on a new 7,200-sentence dataset, with a Krippendorff's alpha of 0.58 between them.
Obligation and Prohibition Extraction Using Hierarchical RNNs
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abstract
We consider the task of detecting contractual obligations and prohibitions. We show that a self-attention mechanism improves the performance of a BILSTM classifier, the previous state of the art for this task, by allowing it to focus on indicative tokens. We also introduce a hierarchical BILSTM, which converts each sentence to an embedding, and processes the sentence embeddings to classify each sentence. Apart from being faster to train, the hierarchical BILSTM outperforms the flat one, even when the latter considers surrounding sentences, because the hierarchical model has a broader discourse view.
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Computational Identification of Regulatory Statements in EU Legislation
A dependency-parsing method and a LEGAL-BERT-based classifier both reach roughly 80-84% accuracy for identifying regulatory statements in EU law on a new 7,200-sentence dataset, with a Krippendorff's alpha of 0.58 between them.