Transformer-Based Named Entity Recognition for French Using Adversarial Adaptation to Similar Domain Corpora (Student Abstract)

Named Entity Recognition (NER) involves the identification and classification of named entities in unstructured text into predefined classes. NER in languages with limited resources, like French, is still an open problem due to the lack of large, robust, labelled datasets. In this paper, we propose a transformer-based NER approach for French using adversarial adaptation to similar domain or general corpora for improved feature extraction and better generalization. We evaluate our approach on three labelled datasets and show that our adaptation framework outperforms the corresponding non-adaptive models for various combinations of transformer models, source datasets and target corpora.

Authors

• Arjun Choudhry Carnegie Mellon University
• Pankaj Gupta Biometric Research Laboratory, Delhi Technological University
• Inder Khatri Biometric Research Laboratory, Delhi Technological University
• Aaryan Gupta Indian Institute of Technology Bombay
• Maxime Nicol IKB Lab, Université du Québec à Montréal
• Marie-Jean Meurs IKB Lab, Université du Québec à Montréal
• Dinesh Kumar Vishwakarma Delhi Technological University

Keywords

• Adversarial Adaptation
• Limited-resource Languages
• Named Entity Recognition
• Transformers
• Unlabelled Corpora