Since data are limited for many of the tasks, domains and languages studied in NLP, transfer learning has gained great prominence in the field as a way to alleviate data scarcity. This thesis presents work on methods, evaluations and resources for multilingual transfer learning. Our research shows how to improve and correctly evaluate cross-lingual embeddings obtained through alignment. It sheds light on the source of performance in cross-lingual transfer learning for dependency parsing. And it introduces two new resources for language generation tasks, one best viewed as a test bed for cross-domain transfer methods and the other, as a test bed for meta-learning techniques.
- AI & Machine learning
- Search and Information retrieval
- Web and Content Management
- Learning Management Systems (LMS)
- EduTech - learning