Legal Named Entity Recognition with Multi-Task Domain Adaptation

Răzvan-Alexandru Smădu, Ion-Robert Dinică, Andrei Marius Avram, Dumitru-Clementin Cercel, Florin Pop, Mihaela-Claudia Cercel

Abstract: Named Entity Recognition (NER) is a well-explored area from Information Retrieval and Natural Language Processing with an extensive research community. Despite that, few languages, such as English and German, are well-resourced, whereas many other languages, such as Romanian, have scarce resources, especially in domain-specific applications. In this work, we address the NER problem in the legal domain from both Romanian and German languages and evaluate the performance of our proposed method based on domain adaptation. We employ multi-task learning to jointly train a neural network on two legal and general domains and perform adaptation among them. The results show that domain adaptation increase performances by a small amount, under 1%, while considerable improvements are in the recall metric.