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Sebastian Ruder | Scaling NLP to the World's Languages | Rise of AI Conference 2022
Explore the challenges and opportunities of scaling NLP to the world's languages, including limited datasets, innovative data collection approaches, and the importance of multimodality, prior knowledge, and human-AI collaboration.
- Mapping language technology to the world’s languages is a long-term project, as indicated by the vast number of languages that are not well-represented in existing datasets.
- Most languages are understated, and large-scale datasets are not feasible or sustainable for all languages, emphasizing the need for smaller, more focused datasets.
- The current state of NLP models is largely limited to English, with a focus on training models that work across multiple languages, incorporating multilingual and multimodal approaches to improve performance.
- The need for multimodality is emphasized, as incorporating different data formats can improve the accuracy of models and their ability to generalize to unseen data.
- Many languages have limited online presence and may not be easily accessible for data collection, highlighting the need for innovative approaches to data collection and annotation.
- The focus should be on identifying critical tasks that are most useful for each language and prioritizing them for improvement.
- Machine learning-based approaches, such as using adapter layers, can help improve the performance of multilingual models and make them more efficient.
- The need for benchmarks that can evaluate text and speech models is emphasized, as existing benchmarks may not accurately reflect the performance of models in real-world scenarios.
- The importance of incorporating prior knowledge and domain-specific knowledge into models is highlighted to improve their generalizability.
- The need for more human-AI collaborative designs is emphasized, as humans can provide valuable insights and feedback to improve AI models.