Nested Named-Entity Recognition in Multilingual Code-Switched NLP

Named-Entity Recognition (NER) is a core part of information extraction. India is a multilingual country with 23 official languages and over 122 major languages, where a significant population is multilingual. Most of the conversations, whether online or in person, involve code-switching and transliterating. Code-switching is the practice of alternating back and forth between two languages or dialects during a conversation or in writing. Processing of such multilingual code-switched text and speech is essential for building intelligent agents and systems that interact with users in multilingual communities. In Natural Language Processing, Indian languages are termed as low-resource languages because of a lack of large-scale supervised data and linguistic resources to make statistical Natural Language Processing viable. We propose a novel data augmentation technique for transfer learning from high-resource languages to low-resource Indian languages with adaptive, behavioral and task-specific fine-tuning on existing pre-trained language representations like mBERT for Nested Named-Entity Recognition in multilingual code-switched natural language processing which can overcome the shortcomings of traditional Named-Entity Recognition methods which fare poorly in multilingual, code-switched and low-resource language contexts. Named-Entity Recognition is highly computationally expensive. In our approach, we also try to bring the computational costs down by employing a unified and robust model.