Multilingual Model
Multilingual models aim to process and generate text across multiple languages, overcoming limitations of monolingual approaches and expanding access to natural language processing (NLP) for low-resource languages. Current research focuses on improving the performance of these models, particularly for low-resource languages, using architectures like transformer-based models (e.g., BERT, mT5) and exploring techniques such as instruction tuning, knowledge distillation, and targeted multilingual adaptation. This work is significant because it addresses biases inherent in predominantly English-centric models and enables broader access to NLP tools and applications across diverse linguistic communities.
Papers
An Initial Investigation of Language Adaptation for TTS Systems under Low-resource Scenarios
Cheng Gong, Erica Cooper, Xin Wang, Chunyu Qiang, Mengzhe Geng, Dan Wells, Longbiao Wang, Jianwu Dang, Marc Tessier, Aidan Pine, Korin Richmond, Junichi Yamagishi
Deep Exploration of Cross-Lingual Zero-Shot Generalization in Instruction Tuning
Janghoon Han, Changho Lee, Joongbo Shin, Stanley Jungkyu Choi, Honglak Lee, Kynghoon Bae
mOSCAR: A Large-scale Multilingual and Multimodal Document-level Corpus
Matthieu Futeral, Armel Zebaze, Pedro Ortiz Suarez, Julien Abadji, Rémi Lacroix, Cordelia Schmid, Rachel Bawden, Benoît Sagot